Projekt Speed Dating
Fran Canjuga, Gašpar Haramija, Leon Hegedić, Josipa Markić
2024-01-21
Uvod
U ovom projektnom zadatku, naziva Speed Dating, analizirat ćemo skup podataka prikupljen između 2002. i 2004. godine u sklopu kolegija Statistička analiza podataka na Fakultetu elektrotehnike i računarstva. Dostupni su dva skupa podataka, a analizirat ćemo ih pomoću deskriptivne statistike i inferencijalne analize kako bismo dobili dublji uvid u ponašanje sudionika i međuodnose varijabli.
Ciljevi ovog projekta su upoznavanje s dostupnim podacima o sudionicima i spojevima te izvlačenje relevantnih informacija pomoću deskriptivne statistike i inferencijalne analize. Paralelno s analizom, planiramo unaprijediti svoje razumijevanje osnovnih metodologija statističke analize podataka i praktičnu primjenu programskog jezika R što ćemo primijeniti na sljedećim hipotezama na temelju kojih ćemo izvući zaključke:
- Je li inteligencija partnera ispitanicima važnija od izgleda?
- Postoji li razlika u interesu za gaming prema zanimanju sudionika?
- Možemo li temeljem drugih varijabli predvidjeti hoće li se sudioniku svidjeti partner?
Počet ćemo s analizom deskriptivne statistike kako bismo stekli osnovni uvid u varijable poput starosti, spola, zanimanja i ocjena partnera. Nakon toga, primijenit ćemo inferencijalnu analizu kako bismo istražili postavljene hipoteze, uključujući pitanja o važnosti inteligencije partnera u odnosu na izgled te razlike u interesu za gaming prema zanimanju sudionika.
Ovaj projekt ima značajnost iz perspektive studenata, poput nas samih, jer nam pruža priliku primijeniti stečeno znanje o statističkoj analizi podataka u stvarnom svijetu. Uvid u ovakav skup podataka može pomoći studentima u donošenju informiranih odluka u svakodnevnom životu, bilo da se radi o razumijevanju međuljudskih odnosa ili donošenju odluka temeljenih na vlastitim preferencijama.
Učitavanje i prikaz podataka
participants = read.csv("participant_data.csv")
dates = read.csv("speed_date_data.csv")
head(participants)## id wave age race gender
## 1 10000 1 21 Asian/Pacific Islander/Asian-American female
## 2 10001 1 24 European/Caucasian-American female
## 3 10002 1 25 European/Caucasian-American female
## 4 10003 1 23 European/Caucasian-American female
## 5 10004 1 21 European/Caucasian-American female
## 6 10005 1 23 Asian/Pacific Islander/Asian-American female
## ambition_important attractive_important funny_important
## 1 15 15 15
## 2 0 45 20
## 3 10 35 10
## 4 10 20 20
## 5 10 20 25
## 6 5 10 25
## intelligence_important shared_interests_important sincere_important field
## 1 20 15 20 Law
## 2 25 5 5 law
## 3 35 0 10 Economics
## 4 20 10 20 Law
## 5 25 15 5 Law
## 6 20 15 25 law
## importance_same_race importance_same_religion sports tvsports exercise dining
## 1 2 4 9 2 8 9
## 2 2 5 3 2 7 10
## 3 8 4 3 8 7 8
## 4 1 1 1 1 6 7
## 5 8 1 7 4 7 7
## 6 1 1 10 8 9 7
## museums art hiking gaming clubbing reading tv theater movies concerts music
## 1 1 1 5 1 5 6 9 1 10 10 9
## 2 8 6 3 5 8 10 1 9 8 7 8
## 3 5 5 8 4 5 7 8 7 7 7 5
## 4 6 7 7 5 7 7 7 9 7 8 7
## 5 6 8 6 6 8 6 8 6 6 3 7
## 6 8 7 9 2 6 9 2 5 6 6 4
## shopping yoga expected_happy_with_sd_people expected_num_interested_in_me
## 1 8 1 3 2
## 2 3 1 4 5
## 3 8 7 4 2
## 4 1 8 1 2
## 5 8 3 7 10
## 6 1 1 4 3
## expected_num_matches attractive sincere intelligence funny ambition X
## 1 4 6 8 8 8 7 NA
## 2 3 7 5 10 8 3 NA
## 3 NA 8 9 8 9 8 NA
## 4 2 7 8 9 7 8 NA
## 5 NA 6 3 6 10 8 NA
## 6 4 5 7 8 9 5 NAhead(dates)## date_id participant_id partner_id attractive_o sincere_o intelligence_o
## 1 100000 10000 10010 6 8 8
## 2 100001 10000 10011 7 8 10
## 3 100002 10000 10012 10 10 10
## 4 100003 10000 10013 7 8 9
## 5 100004 10000 10014 8 7 9
## 6 100005 10000 10015 7 7 8
## funny_o ambitious_o shared_interests_o attractive_partner sincere_partner
## 1 8 8 6 6 9
## 2 7 7 5 7 8
## 3 10 10 10 5 8
## 4 8 9 8 7 6
## 5 6 9 7 5 6
## 6 8 7 7 4 9
## intelligence_partner funny_partner ambition_partner shared_interests_partner
## 1 7 7 6 5
## 2 7 8 5 6
## 3 9 8 5 7
## 4 8 7 6 8
## 5 7 7 6 6
## 6 7 4 6 4
## like guess_prob_liked met decision
## 1 7 6 0 1
## 2 7 5 1 1
## 3 7 NA 1 1
## 4 7 6 0 1
## 5 6 6 0 1
## 6 6 5 0 0Na temelju podataka vidimo da je set podataka za participante sadrži 40 stupaca dok za podatke o spojevima sadrži 19.
Tu se može izbaciti stupce koji nisu uopće potrebni u analizi jer se iz njih ne moze nist zaključiti. Osim toga možemo tu raditi pomoćne datasetove koji će se koristiti u određenim analizama, npr. priprema podataka za linearnu regresiju.
Osim toga promijeniti klase nekih varijabla koje nisu tocne u pocetnim datasetovima…npr. spol mozemo pretvoriti u numericku ako je lakse, “importance_same_race” je klasa character (nije mi jasno zakaj)
U dates data frameu ima dosta podataka koji su NA, odluciti kaj tocno napraviti s njima
Deskriptivna Statistika
# Osnovna deskriptivna statistika:
summary(participants)## id wave age race
## Min. :10000 Min. : 1.00 Min. :18.00 Length:540
## 1st Qu.:10141 1st Qu.: 7.00 1st Qu.:24.00 Class :character
## Median :10276 Median :11.00 Median :26.00 Mode :character
## Mean :10276 Mean :11.11 Mean :26.37
## 3rd Qu.:10413 3rd Qu.:15.25 3rd Qu.:28.00
## Max. :10550 Max. :21.00 Max. :55.00
##
## gender ambition_important attractive_important funny_important
## Length:540 Min. : 0.00 Min. : 0.00 Min. : 0.00
## Class :character 1st Qu.: 5.00 1st Qu.: 15.00 1st Qu.:15.00
## Mode :character Median :10.00 Median : 20.00 Median :18.00
## Mean :10.82 Mean : 22.52 Mean :17.46
## 3rd Qu.:15.00 3rd Qu.: 25.00 3rd Qu.:20.00
## Max. :53.00 Max. :100.00 Max. :50.00
##
## intelligence_important shared_interests_important sincere_important
## Min. : 0.00 Min. : 0.000 Min. : 0.00
## 1st Qu.:17.29 1st Qu.: 8.248 1st Qu.:15.00
## Median :20.00 Median :10.935 Median :18.00
## Mean :20.17 Mean :11.826 Mean :17.31
## 3rd Qu.:23.02 3rd Qu.:16.000 3rd Qu.:20.00
## Max. :50.00 Max. :30.000 Max. :60.00
##
## field importance_same_race importance_same_religion
## Length:540 Length:540 Min. : 1.000
## Class :character Class :character 1st Qu.: 1.000
## Mode :character Mode :character Median : 3.000
## Mean : 3.593
## 3rd Qu.: 6.000
## Max. :10.000
##
## sports tvsports exercise dining
## Min. : 1.000 Min. : 1.00 Min. : 1.000 Min. : 1.000
## 1st Qu.: 4.000 1st Qu.: 2.00 1st Qu.: 5.000 1st Qu.: 7.000
## Median : 7.000 Median : 4.00 Median : 7.000 Median : 8.000
## Mean : 6.409 Mean : 4.55 Mean : 6.278 Mean : 7.778
## 3rd Qu.: 9.000 3rd Qu.: 7.00 3rd Qu.: 8.000 3rd Qu.: 9.000
## Max. :10.000 Max. :10.00 Max. :10.000 Max. :10.000
##
## museums art hiking gaming
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 6.000 1st Qu.: 5.000 1st Qu.: 4.000 1st Qu.: 1.000
## Median : 7.000 Median : 7.000 Median : 6.000 Median : 3.000
## Mean : 6.981 Mean : 6.711 Mean : 5.759 Mean : 3.874
## 3rd Qu.: 8.000 3rd Qu.: 8.000 3rd Qu.: 8.000 3rd Qu.: 6.000
## Max. :10.000 Max. :10.000 Max. :10.000 Max. :14.000
##
## clubbing reading tv theater
## Min. : 0.000 Min. : 1.000 Min. : 1.00 Min. : 0.000
## 1st Qu.: 4.000 1st Qu.: 7.000 1st Qu.: 3.00 1st Qu.: 5.000
## Median : 6.000 Median : 8.000 Median : 6.00 Median : 7.000
## Mean : 5.717 Mean : 7.631 Mean : 5.37 Mean : 6.752
## 3rd Qu.: 8.000 3rd Qu.: 9.000 3rd Qu.: 7.00 3rd Qu.: 9.000
## Max. :10.000 Max. :13.000 Max. :10.00 Max. :10.000
##
## movies concerts music shopping
## Min. : 0.000 Min. : 0.000 Min. : 1.00 Min. : 1.000
## 1st Qu.: 7.000 1st Qu.: 6.000 1st Qu.: 7.00 1st Qu.: 4.000
## Median : 8.000 Median : 7.000 Median : 8.00 Median : 6.000
## Mean : 7.906 Mean : 6.865 Mean : 7.88 Mean : 5.656
## 3rd Qu.: 9.000 3rd Qu.: 8.000 3rd Qu.: 9.00 3rd Qu.: 8.000
## Max. :10.000 Max. :10.000 Max. :10.00 Max. :10.000
##
## yoga expected_happy_with_sd_people expected_num_interested_in_me
## Min. : 0.000 Min. : 1.000 Min. : 0.000
## 1st Qu.: 2.000 1st Qu.: 5.000 1st Qu.: 2.000
## Median : 4.000 Median : 6.000 Median : 4.000
## Mean : 4.433 Mean : 5.491 Mean : 5.869
## 3rd Qu.: 7.000 3rd Qu.: 7.000 3rd Qu.: 8.750
## Max. :10.000 Max. :10.000 Max. :20.000
## NA's :410
## expected_num_matches attractive sincere intelligence
## Min. : 0.000 Min. : 1.000 Min. : 2.000 Min. : 2.000
## 1st Qu.: 2.000 1st Qu.: 6.000 1st Qu.: 8.000 1st Qu.: 7.000
## Median : 3.000 Median : 7.000 Median : 8.000 Median : 8.000
## Mean : 3.043 Mean : 7.052 Mean : 8.275 Mean : 7.714
## 3rd Qu.: 4.000 3rd Qu.: 8.000 3rd Qu.: 9.000 3rd Qu.: 9.000
## Max. :18.000 Max. :10.000 Max. :10.000 Max. :10.000
## NA's :73 NA's :5 NA's :2 NA's :2
## funny ambition X
## Min. : 3.000 Min. : 2.000 Min. :4.000
## 1st Qu.: 8.000 1st Qu.: 7.000 1st Qu.:7.000
## Median : 8.000 Median : 8.000 Median :8.000
## Mean : 8.387 Mean : 7.589 Mean :7.571
## 3rd Qu.: 9.000 3rd Qu.: 9.000 3rd Qu.:9.000
## Max. :10.000 Max. :10.000 Max. :9.000
## NA's :2 NA's :2 NA's :533summary(dates)## date_id participant_id partner_id attractive_o
## Min. :100000 Min. :10000 Min. :10000 Min. : 0.000
## 1st Qu.:102149 1st Qu.:10155 1st Qu.:10155 1st Qu.: 5.000
## Median :104170 Median :10278 Median :10278 Median : 6.000
## Mean :104187 Mean :10282 Mean :10282 Mean : 6.194
## 3rd Qu.:106268 3rd Qu.:10405 3rd Qu.:10405 3rd Qu.: 8.000
## Max. :108377 Max. :10550 Max. :10550 Max. :10.500
## NA's :176
## sincere_o intelligence_o funny_o ambitious_o
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.00
## 1st Qu.: 6.000 1st Qu.: 6.000 1st Qu.: 5.000 1st Qu.: 6.00
## Median : 7.000 Median : 7.000 Median : 7.000 Median : 7.00
## Mean : 7.185 Mean : 7.381 Mean : 6.409 Mean : 6.79
## 3rd Qu.: 8.000 3rd Qu.: 8.000 3rd Qu.: 8.000 3rd Qu.: 8.00
## Max. :10.000 Max. :10.000 Max. :11.000 Max. :10.00
## NA's :247 NA's :268 NA's :321 NA's :678
## shared_interests_o attractive_partner sincere_partner intelligence_partner
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 4.000 1st Qu.: 5.000 1st Qu.: 6.000 1st Qu.: 6.000
## Median : 6.000 Median : 6.000 Median : 7.000 Median : 7.000
## Mean : 5.489 Mean : 6.194 Mean : 7.185 Mean : 7.381
## 3rd Qu.: 7.000 3rd Qu.: 8.000 3rd Qu.: 8.000 3rd Qu.: 8.000
## Max. :10.000 Max. :10.000 Max. :10.000 Max. :10.000
## NA's :1029 NA's :176 NA's :247 NA's :268
## funny_partner ambition_partner shared_interests_partner like
## Min. : 0.000 Min. : 0.00 Min. : 0.000 Min. : 0.000
## 1st Qu.: 5.000 1st Qu.: 6.00 1st Qu.: 4.000 1st Qu.: 5.000
## Median : 7.000 Median : 7.00 Median : 6.000 Median : 6.000
## Mean : 6.409 Mean : 6.79 Mean : 5.489 Mean : 6.142
## 3rd Qu.: 8.000 3rd Qu.: 8.00 3rd Qu.: 7.000 3rd Qu.: 7.000
## Max. :10.000 Max. :10.00 Max. :10.000 Max. :10.000
## NA's :321 NA's :678 NA's :1029 NA's :213
## guess_prob_liked met decision
## Min. : 0.000 Min. :0.0000 Min. :0.0000
## 1st Qu.: 4.000 1st Qu.:0.0000 1st Qu.:0.0000
## Median : 5.000 Median :0.0000 Median :0.0000
## Mean : 5.216 Mean :0.0501 Mean :0.4221
## 3rd Qu.: 7.000 3rd Qu.:0.0000 3rd Qu.:1.0000
## Max. :10.000 Max. :8.0000 Max. :1.0000
## NA's :278 NA's :343Čišćenje dataset:
Nepostojeće vrijednosti:
# sum(is.na(dates$attractive_o))
# sum(is.na(dates$sincere_o))
# sum(is.na(dates$intelligence_o))
# sum(is.na(dates$funny_o))
# sum(is.na(dates$ambitious_o))
# sum(is.na(dates$shared_interests_o))
# sum(is.na(dates$attractive_partner))
# sum(is.na(dates$sincere_partner))
# sum(is.na(dates$intelligence_partner))
# sum(is.na(dates$funny_partner))
# sum(is.na(dates$ambition_partner))
# sum(is.na(dates$shared_interests_partner))
# sum(is.na(dates$like))
# sum(is.na(dates$guess_prob_liked))
# sum(is.na(dates$met))
# sum(is.na(dates$decision))
na_count_per_column <- sapply(dates, function(x) sum(is.na(x)))
data.frame(Column = names(na_count_per_column), NA_Count = na_count_per_column)## Column NA_Count
## date_id date_id 0
## participant_id participant_id 0
## partner_id partner_id 0
## attractive_o attractive_o 176
## sincere_o sincere_o 247
## intelligence_o intelligence_o 268
## funny_o funny_o 321
## ambitious_o ambitious_o 678
## shared_interests_o shared_interests_o 1029
## attractive_partner attractive_partner 176
## sincere_partner sincere_partner 247
## intelligence_partner intelligence_partner 268
## funny_partner funny_partner 321
## ambition_partner ambition_partner 678
## shared_interests_partner shared_interests_partner 1029
## like like 213
## guess_prob_liked guess_prob_liked 278
## met met 343
## decision decision 0dates[!complete.cases(dates),]## date_id participant_id partner_id attractive_o sincere_o intelligence_o
## 3 100002 10000 10012 10.0 10 10.0
## 8 100007 10000 10017 6.0 7 5.0
## 25 100024 10002 10014 6.0 10 8.0
## 26 100025 10002 10015 7.0 6 6.0
## 75 100074 10007 10014 9.0 9 8.0
## 91 100090 10009 10010 7.0 8 7.0
## 92 100091 10009 10011 1.0 1 1.0
## 93 100092 10009 10012 10.0 10 10.0
## 94 100093 10009 10013 7.0 8 7.0
## 95 100094 10009 10014 6.0 9 9.0
## 96 100095 10009 10015 NA 8 7.0
## 97 100096 10009 10016 7.0 7 4.0
## 98 100097 10009 10017 6.0 6 6.0
## 110 100109 10010 10009 6.0 10 10.0
## 120 100119 10011 10009 1.0 1 1.0
## 124 100123 10012 10003 4.0 7 8.0
## 127 100126 10012 10006 3.0 6 8.0
## 130 100129 10012 10009 6.0 10 10.0
## 140 100139 10013 10009 10.0 10 10.0
## 143 100142 10014 10002 9.0 10 10.0
## 148 100147 10014 10007 6.0 8 8.0
## 150 100149 10014 10009 5.0 7 7.0
## 160 100159 10015 10009 5.0 10 6.0
## 170 100169 10016 10009 5.0 10 8.0
## 180 100179 10017 10009 5.0 6 6.0
## 202 100201 10020 10040 5.0 5 5.0
## 203 100202 10020 10041 3.0 10 9.0
## 211 100210 10020 10049 5.0 8 8.0
## 218 100217 10021 10040 5.0 5 5.0
## 224 100223 10021 10046 NA 5 6.0
## 225 100224 10021 10047 4.0 6 6.0
## 226 100225 10021 10048 7.0 8 7.0
## 227 100226 10021 10049 6.0 8 7.0
## 234 100233 10022 10040 8.0 8 8.0
## 235 100234 10022 10041 6.0 10 9.0
## 243 100242 10022 10049 5.0 NA 7.0
## 246 100245 10022 10052 7.0 8 8.0
## 249 100248 10023 10039 5.0 10 9.0
## 250 100249 10023 10040 5.0 5 5.0
## 251 100250 10023 10041 7.0 6 7.0
## 252 100251 10023 10042 5.0 5 6.0
## 253 100252 10023 10043 5.0 7 8.0
## 254 100253 10023 10044 6.0 6 6.0
## 255 100254 10023 10045 7.0 8 8.0
## 257 100256 10023 10047 6.0 8 8.0
## 258 100257 10023 10048 7.0 7 6.0
## 259 100258 10023 10049 7.0 NA NA
## 260 100259 10023 10050 7.0 5 7.0
## 261 100260 10023 10051 7.0 7 9.0
## 262 100261 10023 10052 8.0 8 8.0
## 263 100262 10023 10053 8.0 7 8.0
## 264 100263 10023 10054 8.0 9 9.0
## 266 100265 10024 10040 5.0 5 5.0
## 267 100266 10024 10041 8.0 9 7.0
## 273 100272 10024 10047 9.0 8 7.0
## 275 100274 10024 10049 7.0 NA NA
## 282 100281 10025 10040 5.0 5 5.0
## 283 100282 10025 10041 7.0 8 8.0
## 289 100288 10025 10047 9.0 8 7.0
## 291 100290 10025 10049 8.0 NA NA
## 297 100296 10026 10039 9.0 8 6.0
## 298 100297 10026 10040 8.0 8 8.0
## 299 100298 10026 10041 8.0 9 8.0
## 305 100304 10026 10047 8.0 8 5.0
## 306 100305 10026 10048 8.0 8 7.0
## 307 100306 10026 10049 8.0 NA NA
## 313 100328 10028 10039 5.0 6 3.0
## 314 100329 10028 10040 8.0 8 8.0
## 315 100330 10028 10041 3.0 8 7.0
## 316 100331 10028 10042 4.0 4 5.0
## 319 100334 10028 10045 6.0 7 6.0
## 320 100335 10028 10046 5.0 6 5.0
## 323 100338 10028 10049 6.0 NA NA
## 326 100341 10028 10052 6.0 8 8.0
## 327 100342 10028 10053 5.0 8 7.0
## 330 100345 10029 10040 10.0 10 10.0
## 331 100346 10029 10041 4.0 7 7.0
## 337 100352 10029 10047 5.0 6 5.0
## 338 100353 10029 10048 7.0 7 7.0
## 339 100354 10029 10049 6.0 NA NA
## 346 100361 10030 10040 8.0 8 8.0
## 347 100362 10030 10041 8.0 7 8.0
## 353 100368 10030 10047 7.0 7 7.0
## 354 100369 10030 10048 9.0 7 8.0
## 355 100370 10030 10049 8.0 8 8.0
## 361 100376 10031 10039 8.0 10 10.0
## 362 100377 10031 10040 10.0 10 10.0
## 363 100378 10031 10041 5.0 9 10.0
## 367 100382 10031 10045 8.0 9 8.0
## 371 100386 10031 10049 NA NA NA
## 378 100393 10032 10040 10.0 10 10.0
## 385 100400 10032 10047 5.0 8 8.0
## 393 100408 10033 10039 8.0 7 6.0
## 394 100409 10033 10040 10.0 10 10.0
## 396 100411 10033 10042 6.0 5 5.0
## 399 100414 10033 10045 7.0 7 7.0
## 403 100418 10033 10049 7.0 7 NA
## 407 100422 10033 10053 8.0 8 7.0
## 408 100423 10033 10054 8.0 9 10.0
## 410 100425 10034 10040 10.0 5 10.0
## 413 100428 10034 10043 8.0 7 8.0
## 419 100434 10034 10049 7.0 NA NA
## 426 100441 10035 10040 5.0 5 5.0
## 427 100442 10035 10041 4.0 7 7.0
## 429 100444 10035 10043 7.0 7 7.0
## 435 100450 10035 10049 7.0 NA NA
## 442 100457 10036 10040 5.0 5 5.0
## 443 100458 10036 10041 4.0 8 9.0
## 448 100463 10036 10046 NA 3 8.0
## 451 100466 10036 10049 5.0 NA NA
## 456 100471 10036 10054 6.0 9 9.0
## 458 100473 10037 10040 5.0 5 5.0
## 459 100474 10037 10041 6.0 10 9.0
## 465 100480 10037 10047 7.0 7 7.0
## 467 100482 10037 10049 NA NA NA
## 474 100489 10038 10040 5.0 5 5.0
## 475 100490 10038 10041 4.0 9 8.0
## 479 100494 10038 10045 6.0 8 7.0
## 482 100497 10038 10048 7.0 8 7.0
## 483 100498 10038 10049 NA NA NA
## 484 100499 10038 10050 1.0 8 8.0
## 485 100500 10038 10051 4.0 6 6.0
## 486 100501 10038 10052 5.0 8 8.0
## 487 100502 10038 10053 3.0 8 7.0
## 488 100503 10038 10054 5.0 9 10.0
## 492 100507 10039 10023 5.0 6 7.0
## 495 100510 10039 10026 2.0 6 6.0
## 496 100512 10039 10028 4.0 9 4.0
## 499 100515 10039 10031 4.0 8 7.0
## 501 100517 10039 10033 4.0 4 5.0
## 507 100523 10040 10020 5.0 8 7.0
## 508 100524 10040 10021 4.0 7 6.0
## 509 100525 10040 10022 5.0 7 7.0
## 510 100526 10040 10023 7.0 8 NA
## 511 100527 10040 10024 5.0 8 8.0
## 512 100528 10040 10025 2.0 5 5.0
## 513 100529 10040 10026 1.0 6 4.0
## 514 100531 10040 10028 4.0 8 6.0
## 515 100532 10040 10029 8.0 9 8.0
## 516 100533 10040 10030 3.0 6 6.0
## 517 100534 10040 10031 7.0 8 9.0
## 518 100535 10040 10032 2.0 6 7.0
## 519 100536 10040 10033 3.0 4 7.0
## 520 100537 10040 10034 5.0 9 8.0
## 521 100538 10040 10035 1.0 2 2.0
## 522 100539 10040 10036 2.0 5 6.0
## 523 100540 10040 10037 1.0 1 10.0
## 524 100541 10040 10038 3.0 10 9.0
## 527 100544 10041 10022 6.0 6 7.0
## 528 100545 10041 10023 6.0 8 NA
## 529 100546 10041 10024 6.0 7 8.0
## 530 100547 10041 10025 2.0 5 5.0
## 531 100548 10041 10026 1.0 4 6.0
## 532 100550 10041 10028 6.0 8 7.0
## 533 100551 10041 10029 6.0 7 8.0
## 534 100552 10041 10030 4.0 5 6.0
## 535 100553 10041 10031 5.0 6 6.0
## 539 100557 10041 10035 2.0 2 4.0
## 540 100558 10041 10036 4.0 4 6.0
## 541 100559 10041 10037 8.0 8 10.0
## 542 100560 10041 10038 5.0 5 9.0
## 546 100564 10042 10023 7.0 8 8.0
## 550 100569 10042 10028 5.0 7 5.0
## 555 100574 10042 10033 4.0 4 6.0
## 564 100583 10043 10023 5.0 8 8.0
## 574 100594 10043 10034 5.0 8 7.0
## 582 100602 10044 10023 7.0 7 8.0
## 600 100621 10045 10023 9.0 8 7.0
## 604 100626 10045 10028 8.0 6 6.0
## 607 100629 10045 10031 8.0 9 8.0
## 609 100631 10045 10033 9.0 8 9.0
## 614 100636 10045 10038 8.0 6 9.0
## 616 100638 10046 10021 5.0 7 7.0
## 622 100645 10046 10028 5.0 6 6.0
## 630 100653 10046 10036 3.0 5 7.0
## 634 100657 10047 10021 7.0 7 7.0
## 636 100659 10047 10023 7.0 7 7.0
## 637 100660 10047 10024 7.0 8 8.0
## 638 100661 10047 10025 6.0 5 5.0
## 639 100662 10047 10026 4.0 6 4.0
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## 644 100668 10047 10032 8.0 7 6.0
## 649 100673 10047 10037 8.0 6 5.0
## 652 100676 10048 10021 6.0 7 7.0
## 654 100678 10048 10023 6.0 NA NA
## 657 100681 10048 10026 4.0 5 4.0
## 659 100684 10048 10029 8.0 7 7.0
## 660 100685 10048 10030 7.0 8 8.0
## 668 100693 10048 10038 5.0 7 7.0
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## 670 100695 10049 10021 5.0 6 7.0
## 671 100696 10049 10022 6.0 6 7.0
## 672 100697 10049 10023 6.0 8 NA
## 673 100698 10049 10024 9.0 8 9.0
## 674 100699 10049 10025 7.0 6 6.0
## 675 100700 10049 10026 6.0 5 6.0
## 676 100702 10049 10028 7.0 7 7.0
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## 685 100711 10049 10037 8.0 8 8.0
## 686 100712 10049 10038 5.0 6 8.0
## 690 100716 10050 10023 6.0 6 8.0
## 704 100731 10050 10038 3.0 1 8.0
## 708 100735 10051 10023 4.0 NA NA
## 720 100748 10051 10036 2.0 6 6.0
## 722 100750 10051 10038 3.0 9 9.0
## 725 100753 10052 10022 NA NA NA
## 726 100754 10052 10023 8.0 NA NA
## 730 100759 10052 10028 9.0 8 8.0
## 740 100769 10052 10038 8.0 7 9.0
## 744 100773 10053 10023 6.0 8 NA
## 758 100788 10053 10038 4.0 8 8.0
## 762 100792 10054 10023 8.0 7 7.0
## 774 100805 10054 10036 5.0 7 6.0
## 776 100807 10054 10038 6.0 7 8.0
## 779 100810 10055 10067 6.0 4 5.0
## 780 100811 10055 10068 7.0 5 5.0
## 790 100821 10056 10068 4.0 7 7.0
## 797 100848 10059 10065 5.0 8 7.0
## 798 100849 10059 10066 6.0 6 5.0
## 800 100851 10059 10068 4.0 7 7.0
## 804 100855 10059 10072 4.0 7 6.0
## 806 100857 10059 10074 5.0 6 7.0
## 807 100858 10060 10065 3.0 6 NA
## 820 100871 10061 10068 8.0 6 8.0
## 822 100873 10061 10070 10.0 6 8.0
## 823 100874 10061 10071 7.0 7 7.0
## 830 100881 10062 10068 1.0 6 8.0
## 832 100883 10062 10070 6.0 4 6.0
## 844 100895 10063 10072 3.0 7 6.0
## 859 100912 10065 10059 9.0 10 10.0
## 860 100913 10065 10060 5.0 8 7.0
## 867 100922 10066 10059 7.0 10 10.0
## 881 100938 10068 10055 5.0 10 8.0
## 882 100939 10068 10056 9.0 8 7.0
## 883 100942 10068 10059 9.0 9 10.0
## 885 100944 10068 10061 8.0 8 8.0
## 886 100945 10068 10062 6.0 7 8.0
## 901 100964 10070 10061 7.0 8 8.0
## 902 100965 10070 10062 1.0 7 5.0
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## 916 100983 10072 10060 6.0 5 7.0
## 931 101002 10074 10059 2.0 10 10.0
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## 938 101009 10075 10094 6.0 6 7.0
## 939 101010 10075 10095 6.0 7 7.0
## 940 101011 10075 10096 4.0 6 7.0
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## 944 101015 10075 10100 7.0 7 8.0
## 945 101016 10075 10101 5.0 7 8.0
## 946 101017 10075 10102 6.0 8 9.0
## 947 101018 10075 10103 6.0 8 7.0
## 948 101019 10075 10104 7.0 7 8.0
## 949 101020 10075 10105 8.0 3 7.0
## 950 101021 10075 10106 9.0 9 9.0
## 952 101023 10075 10108 8.0 10 8.0
## 953 101024 10075 10109 6.0 6 8.0
## 954 101025 10075 10110 3.0 4 8.0
## 957 101028 10076 10095 7.0 7 7.0
## 959 101030 10076 10097 10.0 10 10.0
## 961 101032 10076 10099 8.0 7 6.0
## 962 101033 10076 10100 7.0 NA 6.0
## 967 101038 10076 10105 7.0 5 7.0
## 975 101046 10077 10095 6.0 6 6.0
## 976 101047 10077 10096 8.0 7 8.0
## 977 101048 10077 10097 10.0 10 10.0
## 978 101049 10077 10098 4.0 4 3.0
## 979 101050 10077 10099 8.0 8 7.0
## 980 101051 10077 10100 7.0 NA 6.0
## 981 101052 10077 10101 5.0 5 6.0
## 982 101053 10077 10102 6.0 7 6.0
## 985 101056 10077 10105 10.0 10 10.0
## 989 101060 10077 10109 5.0 5 7.0
## 990 101061 10077 10110 4.0 4 5.0
## 995 101066 10078 10097 10.0 10 10.0
## 998 101069 10078 10100 6.0 8 8.0
## 1001 101072 10078 10103 6.0 8 7.0
## 1007 101078 10078 10109 5.0 8 9.0
## 1009 101080 10079 10093 6.0 7 7.0
## 1011 101082 10079 10095 6.0 7 7.0
## 1012 101083 10079 10096 4.0 6 7.0
## 1013 101084 10079 10097 10.0 10 10.0
## 1016 101087 10079 10100 6.0 8 7.0
## 1021 101092 10079 10105 8.0 10 7.0
## 1025 101096 10079 10109 6.0 8 8.0
## 1029 101100 10080 10095 7.0 7 7.0
## 1031 101102 10080 10097 10.0 10 10.0
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## 1049 101120 10081 10097 10.0 10 10.0
## 1057 101128 10081 10105 6.0 10 5.0
## 1065 101136 10082 10095 7.0 7 7.0
## 1067 101138 10082 10097 10.0 10 10.0
## 1070 101141 10082 10100 8.0 8 8.0
## 1079 101150 10082 10109 7.0 8 9.0
## 1085 101156 10083 10097 10.0 10 10.0
## 1088 101159 10083 10100 8.0 9 8.0
## 1097 101168 10083 10109 8.0 6 7.0
## 1101 101172 10084 10095 7.0 7 7.0
## 1103 101174 10084 10097 10.0 10 10.0
## 1106 101177 10084 10100 7.0 7 7.0
## 1111 101182 10084 10105 6.0 10 6.0
## 1115 101186 10084 10109 6.0 8 8.0
## 1117 101188 10085 10093 6.0 8 8.0
## 1119 101190 10085 10095 8.0 8 9.0
## 1121 101192 10085 10097 10.0 10 10.0
## 1124 101195 10085 10100 9.0 8 8.0
## 1134 101205 10085 10110 5.0 6 7.0
## 1137 101208 10086 10095 7.0 6 6.0
## 1139 101210 10086 10097 10.0 10 10.0
## 1142 101213 10086 10100 8.0 6 6.0
## 1147 101218 10086 10105 7.0 3 6.0
## 1151 101222 10086 10109 7.0 8 8.0
## 1155 101226 10087 10095 7.0 7 7.0
## 1157 101228 10087 10097 10.0 10 10.0
## 1160 101231 10087 10100 8.0 8 8.0
## 1173 101244 10088 10095 6.0 7 7.0
## 1175 101246 10088 10097 10.0 10 10.0
## 1183 101254 10088 10105 8.0 8 8.0
## 1191 101262 10089 10095 6.0 8 7.0
## 1193 101264 10089 10097 10.0 10 10.0
## 1201 101272 10089 10105 6.0 10 8.0
## 1205 101276 10089 10109 5.0 6 8.0
## 1207 101278 10090 10093 9.0 8 8.0
## 1208 101279 10090 10094 7.0 7 8.0
## 1210 101281 10090 10096 8.0 8 8.0
## 1211 101282 10090 10097 10.0 10 10.0
## 1212 101283 10090 10098 9.0 8 8.0
## 1213 101284 10090 10099 9.0 7 9.0
## 1214 101285 10090 10100 8.0 7 7.0
## 1216 101287 10090 10102 8.0 8 8.0
## 1217 101288 10090 10103 8.0 7 7.0
## 1218 101289 10090 10104 8.0 8 10.0
## 1219 101290 10090 10105 10.0 10 10.0
## 1220 101291 10090 10106 8.0 9 8.0
## 1222 101293 10090 10108 10.0 10 8.0
## 1223 101294 10090 10109 8.0 8 8.0
## 1224 101295 10090 10110 8.0 7 8.0
## 1227 101298 10091 10095 7.0 7 7.0
## 1229 101300 10091 10097 10.0 10 10.0
## 1232 101303 10091 10100 9.0 8 8.0
## 1237 101308 10091 10105 6.0 10 4.0
## 1241 101312 10091 10109 9.0 NA 8.0
## 1245 101316 10092 10095 7.0 8 8.0
## 1246 101317 10092 10096 8.0 8 8.0
## 1247 101318 10092 10097 10.0 10 10.0
## 1248 101319 10092 10098 5.0 5 7.0
## 1250 101321 10092 10100 9.0 8 8.0
## 1261 101332 10093 10075 5.0 5 5.0
## 1265 101336 10093 10079 1.0 NA 8.0
## 1271 101342 10093 10085 5.0 7 8.0
## 1276 101347 10093 10090 5.0 7 7.0
## 1279 101350 10094 10075 4.0 5 5.0
## 1294 101365 10094 10090 5.0 6 6.0
## 1297 101368 10095 10075 2.0 10 4.0
## 1298 101369 10095 10076 4.0 6 7.0
## 1299 101370 10095 10077 2.0 5 9.0
## 1301 101372 10095 10079 6.0 7 8.0
## 1302 101373 10095 10080 2.0 6 8.0
## 1303 101374 10095 10081 5.0 10 8.0
## 1304 101375 10095 10082 4.0 8 8.0
## 1306 101377 10095 10084 5.0 5 6.0
## 1307 101378 10095 10085 6.0 7 7.0
## 1308 101379 10095 10086 3.0 6 6.0
## 1309 101380 10095 10087 3.0 7 9.0
## 1310 101381 10095 10088 6.0 10 9.0
## 1311 101382 10095 10089 5.0 5 6.0
## 1313 101384 10095 10091 3.0 9 10.0
## 1314 101385 10095 10092 7.0 7 7.0
## 1315 101386 10096 10075 10.0 8 10.0
## 1317 101388 10096 10077 9.0 5 9.0
## 1319 101390 10096 10079 8.0 8 8.0
## 1322 101393 10096 10082 8.0 6 7.0
## 1327 101398 10096 10087 6.0 7 10.0
## 1330 101401 10096 10090 6.0 7 8.0
## 1332 101403 10096 10092 8.0 7 8.0
## 1333 101404 10097 10075 2.0 10 5.0
## 1334 101405 10097 10076 8.0 9 9.0
## 1335 101406 10097 10077 7.0 9 9.0
## 1336 101407 10097 10078 2.0 2 2.0
## 1337 101408 10097 10079 6.0 7 7.0
## 1338 101409 10097 10080 7.0 6 6.0
## 1339 101410 10097 10081 7.0 10 7.0
## 1340 101411 10097 10082 5.0 6 7.0
## 1341 101412 10097 10083 5.0 8 5.0
## 1342 101413 10097 10084 8.0 8 8.0
## 1343 101414 10097 10085 7.0 8 7.0
## 1344 101415 10097 10086 2.0 8 8.0
## 1345 101416 10097 10087 3.0 10 10.0
## 1346 101417 10097 10088 6.0 9 7.0
## 1347 101418 10097 10089 6.0 6 6.0
## 1348 101419 10097 10090 6.0 9 8.0
## 1349 101420 10097 10091 8.0 7 8.0
## 1350 101421 10097 10092 6.0 6 6.0
## 1351 101422 10098 10075 6.0 8 8.0
## 1353 101424 10098 10077 7.0 9 9.0
## 1365 101436 10098 10089 7.0 8 7.0
## 1366 101437 10098 10090 8.0 7 8.0
## 1368 101439 10098 10092 7.0 6 7.0
## 1369 101440 10099 10075 1.0 10 NA
## 1370 101441 10099 10076 4.0 6 6.0
## 1371 101442 10099 10077 2.0 9 9.0
## 1384 101455 10099 10090 6.0 7 8.0
## 1387 101458 10100 10075 1.0 10 5.0
## 1388 101459 10100 10076 3.0 5 6.0
## 1389 101460 10100 10077 2.0 9 9.0
## 1390 101461 10100 10078 4.0 6 6.0
## 1391 101462 10100 10079 6.0 8 7.0
## 1392 101463 10100 10080 3.0 8 9.0
## 1394 101465 10100 10082 5.0 8 8.0
## 1395 101466 10100 10083 3.0 7 7.0
## 1396 101467 10100 10084 4.0 7 9.0
## 1397 101468 10100 10085 6.0 7 7.0
## 1398 101469 10100 10086 1.0 3 4.0
## 1399 101470 10100 10087 3.0 10 10.0
## 1402 101473 10100 10090 4.0 7 8.0
## 1403 101474 10100 10091 6.0 9 10.0
## 1404 101475 10100 10092 7.0 6 7.0
## 1405 101476 10101 10075 1.0 NA 8.0
## 1407 101478 10101 10077 3.0 8 8.0
## 1423 101494 10102 10075 6.0 NA 5.0
## 1425 101496 10102 10077 5.0 9 9.0
## 1438 101509 10102 10090 7.0 7 8.0
## 1441 101512 10103 10075 2.0 3 5.0
## 1444 101515 10103 10078 NA 6 7.0
## 1456 101527 10103 10090 7.0 7 7.0
## 1459 101530 10104 10075 5.0 7 8.0
## 1477 101548 10105 10075 4.0 8 8.0
## 1478 101549 10105 10076 5.0 6 7.0
## 1479 101550 10105 10077 5.0 9 9.0
## 1481 101552 10105 10079 7.0 9 8.0
## 1483 101554 10105 10081 5.0 10 9.0
## 1486 101557 10105 10084 6.0 8 7.0
## 1488 101559 10105 10086 2.0 4 5.0
## 1490 101561 10105 10088 8.0 10 10.0
## 1491 101562 10105 10089 6.0 7 7.0
## 1492 101563 10105 10090 5.0 7 8.0
## 1493 101564 10105 10091 7.0 8 8.0
## 1495 101566 10106 10075 8.0 6 7.0
## 1510 101581 10106 10090 8.0 7 8.0
## 1531 101602 10108 10075 5.0 5 6.0
## 1546 101617 10108 10090 6.0 7 8.0
## 1549 101620 10109 10075 5.0 5 6.0
## 1551 101622 10109 10077 2.0 2 5.0
## 1552 101623 10109 10078 4.0 4 4.0
## 1553 101624 10109 10079 6.0 NA 8.0
## 1556 101627 10109 10082 4.0 7 8.0
## 1557 101628 10109 10083 4.0 4 5.0
## 1558 101629 10109 10084 5.0 5 5.0
## 1560 101631 10109 10086 4.0 3 6.0
## 1563 101634 10109 10089 6.0 6 6.0
## 1564 101635 10109 10090 6.0 7 8.0
## 1565 101636 10109 10091 6.0 9 9.0
## 1567 101638 10110 10075 2.0 8 8.0
## 1569 101640 10110 10077 5.0 5 9.0
## 1577 101648 10110 10085 4.0 5 5.0
## 1582 101653 10110 10090 5.0 8 8.0
## 1616 101695 10114 10129 NA NA NA
## 1626 101707 10116 10121 6.0 6 5.0
## 1627 101708 10116 10122 8.0 7 6.0
## 1632 101715 10116 10129 NA NA NA
## 1633 101716 10117 10120 9.0 9 8.0
## 1634 101717 10117 10121 6.0 4 5.0
## 1635 101718 10117 10122 9.0 8 4.0
## 1636 101719 10117 10123 8.0 8 6.0
## 1637 101720 10117 10124 6.0 9 8.0
## 1639 101722 10117 10126 9.0 8 9.0
## 1640 101725 10117 10129 6.0 6 6.0
## 1663 101752 10120 10117 3.0 8 5.0
## 1672 101762 10121 10117 NA 6 7.0
## 1681 101772 10122 10117 4.0 9 8.0
## 1699 101792 10124 10117 5.0 4 8.0
## 1704 101798 10125 10113 2.0 3 2.0
## 1717 101812 10126 10117 5.0 7 NA
## 1723 101839 10129 10114 NA NA NA
## 1725 101841 10129 10116 NA NA NA
## 1726 101842 10129 10117 2.0 7 6.0
## 1728 101844 10129 10119 7.0 10 10.0
## 1731 101848 10130 10137 8.0 NA 8.0
## 1736 101853 10131 10137 6.0 8 10.0
## 1741 101858 10132 10137 7.0 9 9.0
## 1742 101859 10132 10138 6.5 8 7.5
## 1744 101861 10133 10135 7.0 8 8.0
## 1745 101862 10133 10136 4.0 6 6.0
## 1746 101863 10133 10137 6.0 NA NA
## 1747 101864 10133 10138 6.5 8 8.0
## 1748 101865 10133 10139 8.0 9 8.0
## 1752 101874 10135 10133 8.0 5 NA
## 1756 101879 10136 10133 8.0 8 8.0
## 1757 101881 10137 10130 5.0 7 9.0
## 1758 101882 10137 10131 6.0 5 5.0
## 1759 101883 10137 10132 7.0 7 8.0
## 1760 101884 10137 10133 7.0 8 NA
## 1763 101888 10138 10132 8.0 9 9.0
## 1764 101889 10138 10133 8.0 8 8.0
## 1768 101894 10139 10133 7.0 NA NA
## 1770 101897 10140 10157 7.0 7 7.0
## 1771 101898 10140 10158 6.0 9 8.0
## 1775 101902 10140 10162 8.0 7 9.0
## 1777 101904 10140 10164 4.0 4 8.0
## 1785 101912 10141 10156 6.0 8 9.0
## 1786 101913 10141 10157 5.0 7 7.0
## 1789 101916 10141 10160 3.0 6 6.0
## 1791 101918 10141 10162 4.0 9 9.0
## 1792 101919 10141 10163 6.0 9 9.0
## 1793 101920 10141 10164 3.0 7 7.0
## 1794 101921 10141 10165 3.0 7 6.0
## 1795 101922 10141 10166 5.0 5 5.0
## 1799 101926 10141 10170 NA 8 9.0
## 1800 101927 10141 10171 4.0 6 5.0
## 1809 101936 10142 10164 7.0 7 9.0
## 1813 101940 10142 10168 NA NA NA
## 1823 101950 10143 10162 4.0 8 9.0
## 1825 101952 10143 10164 5.0 7 7.0
## 1833 101960 10144 10156 8.0 9 8.0
## 1834 101961 10144 10157 6.0 8 8.0
## 1835 101962 10144 10158 5.0 8 NA
## 1836 101963 10144 10159 5.0 6 7.0
## 1837 101964 10144 10160 4.0 6 8.0
## 1838 101965 10144 10161 5.0 8 7.0
## 1840 101967 10144 10163 8.0 7 9.0
## 1841 101968 10144 10164 7.0 7 7.0
## 1842 101969 10144 10165 3.0 6 8.0
## 1843 101970 10144 10166 4.0 6 7.0
## 1844 101971 10144 10167 7.0 8 9.0
## 1845 101972 10144 10168 5.0 7 7.0
## 1846 101973 10144 10169 6.0 8 9.0
## 1847 101974 10144 10170 1.0 4 5.0
## 1848 101975 10144 10171 5.0 6 5.0
## 1857 101984 10145 10164 8.0 8 8.0
## 1860 101987 10145 10167 10.0 8 8.0
## 1863 101990 10145 10170 8.0 4 NA
## 1866 101993 10146 10157 7.0 6 6.0
## 1871 101998 10146 10162 7.0 7 7.0
## 1873 102000 10146 10164 8.0 8 8.0
## 1876 102003 10146 10167 9.0 9 9.0
## 1881 102008 10147 10156 9.0 9 9.0
## 1883 102010 10147 10158 6.0 9 9.0
## 1885 102012 10147 10160 8.0 6 6.0
## 1887 102014 10147 10162 6.0 6 6.0
## 1888 102015 10147 10163 9.0 7 9.0
## 1889 102016 10147 10164 7.0 7 7.0
## 1891 102018 10147 10166 5.0 6 6.0
## 1893 102020 10147 10168 7.0 7 7.0
## 1894 102021 10147 10169 8.0 7 8.0
## 1895 102022 10147 10170 6.0 4 7.0
## 1896 102023 10147 10171 8.0 8 8.0
## 1897 102024 10148 10156 10.0 8 9.0
## 1898 102025 10148 10157 8.0 9 9.0
## 1902 102029 10148 10161 9.0 9 9.0
## 1903 102030 10148 10162 9.0 10 9.0
## 1904 102031 10148 10163 8.0 10 9.0
## 1905 102032 10148 10164 5.0 7 9.0
## 1907 102034 10148 10166 8.0 7 7.0
## 1910 102037 10148 10169 7.0 9 9.0
## 1911 102038 10148 10170 4.0 6 8.0
## 1912 102039 10148 10171 7.0 7 7.0
## 1914 102041 10149 10157 8.0 8 7.0
## 1915 102042 10149 10158 8.0 8 NA
## 1919 102046 10149 10162 9.0 9 7.0
## 1921 102048 10149 10164 8.0 8 8.0
## 1924 102051 10149 10167 9.0 9 9.0
## 1926 102053 10149 10169 9.0 9 8.0
## 1930 102057 10150 10157 7.0 7 7.0
## 1933 102060 10150 10160 6.0 7 7.0
## 1934 102061 10150 10161 6.0 8 8.0
## 1935 102062 10150 10162 8.0 9 9.0
## 1937 102064 10150 10164 8.0 8 NA
## 1938 102065 10150 10165 7.0 8 8.0
## 1941 102068 10150 10168 5.0 7 6.0
## 1942 102069 10150 10169 7.0 7 8.0
## 1943 102070 10150 10170 7.0 8 8.0
## 1945 102072 10151 10156 7.0 9 8.0
## 1946 102073 10151 10157 7.0 7 7.0
## 1947 102074 10151 10158 7.0 9 8.0
## 1948 102075 10151 10159 7.0 9 6.0
## 1951 102078 10151 10162 7.0 8 8.0
## 1952 102079 10151 10163 7.0 8 8.0
## 1953 102080 10151 10164 6.0 7 7.0
## 1954 102081 10151 10165 4.0 8 6.0
## 1955 102082 10151 10166 7.0 7 7.0
## 1956 102083 10151 10167 9.0 10 8.0
## 1957 102084 10151 10168 5.0 7 7.0
## 1958 102085 10151 10169 6.0 8 8.0
## 1959 102086 10151 10170 4.0 6 8.0
## 1960 102087 10151 10171 5.0 5 5.0
## 1967 102094 10152 10162 8.0 7 7.0
## 1969 102096 10152 10164 7.0 7 7.0
## 1978 102105 10153 10157 7.0 9 8.0
## 1979 102106 10153 10158 7.0 8 8.0
## 1983 102110 10153 10162 5.0 10 8.0
## 1985 102112 10153 10164 7.0 7 7.0
## 1994 102121 10154 10157 6.0 7 7.0
## 2001 102128 10154 10164 7.0 7 7.0
## 2010 102137 10155 10157 6.0 10 6.0
## 2015 102142 10155 10162 8.0 7 8.0
## 2017 102144 10155 10164 8.0 8 8.0
## 2026 102153 10156 10141 5.0 6 8.0
## 2029 102156 10156 10144 4.0 6 8.0
## 2032 102159 10156 10147 6.0 7 7.0
## 2033 102160 10156 10148 5.0 5 8.0
## 2036 102163 10156 10151 6.0 6 8.0
## 2041 102168 10157 10140 5.0 10 9.0
## 2042 102169 10157 10141 4.0 6 8.0
## 2045 102172 10157 10144 5.0 7 10.0
## 2047 102174 10157 10146 3.0 8 8.0
## 2049 102176 10157 10148 5.0 8 10.0
## 2050 102177 10157 10149 3.0 7 7.0
## 2051 102178 10157 10150 4.0 6 5.0
## 2052 102179 10157 10151 5.0 5 6.0
## 2054 102181 10157 10153 1.0 4 6.0
## 2055 102182 10157 10154 3.0 10 10.0
## 2056 102183 10157 10155 2.0 6 6.0
## 2057 102184 10158 10140 8.0 8 8.0
## 2061 102188 10158 10144 8.0 10 7.0
## 2064 102191 10158 10147 9.0 7 8.0
## 2066 102193 10158 10149 8.0 6 8.0
## 2068 102195 10158 10151 5.0 6 6.0
## 2070 102197 10158 10153 7.0 7 6.0
## 2077 102204 10159 10144 6.0 8 8.0
## 2084 102211 10159 10151 7.0 7 7.0
## 2090 102217 10160 10141 6.0 7 9.0
## 2093 102220 10160 10144 5.0 5 10.0
## 2096 102223 10160 10147 4.0 8 7.0
## 2099 102226 10160 10150 5.0 6 8.0
## 2109 102236 10161 10144 6.0 8 8.0
## 2113 102240 10161 10148 7.0 7 8.0
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## 2284 102411 10172 10185 6.0 8 NA
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## 2294 102421 10173 10185 6.0 NA 10.0
## 2304 102431 10174 10185 NA NA NA
## 2307 102434 10174 10188 9.0 NA 8.0
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## 2317 102444 10175 10188 5.0 NA 5.0
## 2321 102448 10176 10182 8.0 10 10.0
## 2322 102449 10176 10183 3.0 7 7.0
## 2323 102450 10176 10184 5.0 6 6.0
## 2324 102451 10176 10185 NA NA NA
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## 2327 102454 10176 10188 3.0 9 8.0
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## 2353 102480 10179 10184 6.0 8 8.0
## 2354 102481 10179 10185 NA NA NA
## 2357 102484 10179 10188 6.0 9 NA
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## 2367 102494 10180 10188 7.0 5 7.0
## 2368 102495 10180 10189 5.0 7 7.0
## 2374 102501 10181 10185 4.0 NA NA
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## 2907 103034 10213 10198 6.0 8 8.0
## 2916 103043 10213 10207 8.0 NA 8.0
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## 2948 103075 10215 10199 7.0 8 10.0
## 2956 103083 10215 10207 6.0 6 9.0
## 2957 103084 10215 10208 9.0 NA 9.0
## 2963 103090 10216 10194 2.0 3 3.0
## 2966 103093 10216 10197 4.0 9 9.0
## 2967 103094 10216 10198 6.0 7 9.0
## 2968 103095 10216 10199 2.0 8 NA
## 2970 103097 10216 10201 7.0 5 10.0
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## 2977 103104 10216 10208 6.0 10 NA
## 2981 103108 10217 10192 8.0 9 8.0
## 2987 103114 10217 10198 7.0 9 8.0
## 2988 103115 10217 10199 7.0 NA NA
## 2989 103116 10217 10200 8.0 8 9.0
## 2990 103117 10217 10201 8.0 6 9.0
## 2993 103120 10217 10204 7.0 7 7.0
## 2994 103121 10217 10205 9.0 9 9.0
## 2995 103122 10217 10206 8.0 9 9.0
## 2996 103123 10217 10207 6.0 5 NA
## 2997 103124 10217 10208 9.0 8 5.0
## 3003 103130 10218 10194 4.0 4 3.0
## 3007 103134 10218 10198 6.0 10 9.0
## 3016 103143 10218 10207 5.0 7 6.0
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## 3047 103174 10220 10198 3.0 9 10.0
## 3048 103175 10220 10199 1.0 7 NA
## 3056 103183 10220 10207 3.0 NA 7.0
## 3057 103184 10220 10208 3.0 NA 9.0
## 3063 103190 10221 10194 2.0 7 7.0
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## 3068 103195 10221 10199 6.0 8 9.0
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## 3167 103294 10226 10198 5.0 10 9.0
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## 3170 103297 10226 10201 6.0 7 10.0
## 3176 103303 10226 10207 5.0 6 7.0
## 3177 103304 10226 10208 5.0 8 NA
## 3183 103310 10227 10194 3.0 3 5.0
## 3187 103314 10227 10198 7.0 10 8.0
## 3188 103315 10227 10199 7.0 7 8.0
## 3196 103323 10227 10207 8.0 NA 8.0
## 3197 103324 10227 10208 10.0 9 9.0
## 3203 103330 10228 10194 2.0 3 3.0
## 3207 103334 10228 10198 8.0 7 NA
## 3216 103343 10228 10207 5.0 5 7.0
## 3217 103344 10228 10208 8.0 10 NA
## 3223 103350 10229 10194 3.0 6 7.0
## 3227 103354 10229 10198 5.0 7 8.0
## 3235 103362 10229 10206 5.0 9 9.0
## 3236 103363 10229 10207 8.0 8 8.0
## 3237 103364 10229 10208 5.0 2 3.0
## 3243 103370 10230 10194 3.0 5 4.0
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## 3248 103375 10230 10199 4.0 8 8.0
## 3250 103377 10230 10201 5.0 6 8.0
## 3253 103380 10230 10204 4.0 9 7.0
## 3256 103383 10230 10207 5.0 NA NA
## 3257 103384 10230 10208 3.0 9 9.0
## 3261 103388 10231 10192 6.0 7 9.0
## 3262 103389 10231 10193 6.0 9 10.0
## 3263 103390 10231 10194 2.0 8 7.0
## 3264 103391 10231 10195 7.0 7 7.0
## 3265 103392 10231 10196 4.0 6 6.0
## 3266 103393 10231 10197 8.0 10 10.0
## 3267 103394 10231 10198 NA NA NA
## 3268 103395 10231 10199 4.0 8 8.0
## 3269 103396 10231 10200 7.0 8 8.0
## 3270 103397 10231 10201 6.0 10 9.0
## 3271 103398 10231 10202 6.0 8 9.0
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## 3275 103402 10231 10206 6.0 8 8.0
## 3276 103403 10231 10207 5.0 NA 7.0
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## 3278 103405 10231 10209 5.0 5 7.0
## 3279 103406 10231 10210 6.0 7 7.0
## 3280 103407 10231 10211 8.0 8 8.0
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## 3298 103425 10233 10249 8.0 9 8.0
## 3299 103426 10234 10241 6.0 8 8.0
## 3302 103429 10234 10244 2.0 3 NA
## 3307 103434 10234 10249 4.0 8 7.0
## 3308 103435 10235 10241 7.0 8 7.0
## 3309 103436 10235 10242 2.0 6 8.0
## 3310 103437 10235 10243 7.0 8 6.0
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## 3396 103523 10244 10239 4.0 9 8.0
## 3397 103524 10244 10240 4.0 8 8.0
## 3398 103525 10245 10232 3.0 7 9.0
## 3424 103551 10247 10240 3.0 5 NA
## 3434 103561 10249 10232 5.0 8 7.0
## 3435 103562 10249 10233 5.0 9 8.0
## 3436 103563 10249 10234 5.0 6 6.0
## 3437 103564 10249 10235 2.0 10 10.0
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## 3440 103567 10249 10238 5.0 5 5.0
## 3441 103568 10249 10239 7.0 7 9.0
## 3447 103574 10250 10275 6.0 8 7.0
## 3454 103581 10250 10282 7.0 7 NA
## 3455 103582 10250 10283 5.0 5 5.0
## 3456 103583 10250 10284 9.0 8 8.0
## 3457 103584 10250 10285 7.0 7 7.0
## 3462 103589 10250 10290 6.0 10 6.0
## 3468 103595 10251 10275 5.0 7 7.0
## 3473 103600 10251 10280 6.0 6 9.0
## 3475 103602 10251 10282 2.0 5 NA
## 3477 103604 10251 10284 6.0 NA NA
## 3478 103605 10251 10285 4.0 7 6.0
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## 3496 103623 10252 10282 3.0 4 NA
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## 1723 NA NA NA NA NA
## 1725 NA NA NA NA NA
## 1726 2.0 NA 1.0 6.0 6
## 1728 8.0 10.0 1.0 7.0 5
## 1731 8.0 7.0 NA 5.0 7
## 1736 6.0 8.0 NA 6.0 5
## 1741 7.0 6.0 NA 7.0 7
## 1742 7.0 7.0 NA 8.0 9
## 1744 7.0 7.0 6.0 8.0 5
## 1745 1.0 7.0 1.0 8.0 8
## 1746 6.0 5.0 NA 7.0 8
## 1747 7.0 7.0 7.0 8.0 8
## 1748 8.0 6.0 5.0 7.0 NA
## 1752 NA NA 5.0 7.0 8
## 1756 NA NA 2.0 4.0 6
## 1757 5.0 9.0 5.0 8.0 NA
## 1758 6.0 6.0 7.0 6.0 8
## 1759 5.0 8.0 5.0 7.0 9
## 1760 8.0 4.0 6.0 6.0 NA
## 1763 9.0 10.0 7.0 6.5 8
## 1764 8.0 NA NA 6.5 8
## 1768 8.0 8.0 NA 8.0 9
## 1770 6.0 NA 4.0 5.0 10
## 1771 7.0 9.0 NA 8.0 8
## 1775 7.0 NA NA 8.0 9
## 1777 4.0 NA NA 8.0 6
## 1785 6.0 8.0 7.0 5.0 6
## 1786 5.0 4.0 4.0 4.0 6
## 1789 2.0 7.0 2.0 6.0 7
## 1791 5.0 NA NA 8.0 7
## 1792 6.0 8.0 4.0 10.0 9
## 1793 5.0 NA NA 9.0 8
## 1794 4.0 5.0 2.0 5.0 6
## 1795 6.0 6.0 6.0 7.0 8
## 1799 2.0 8.0 1.0 8.0 8
## 1800 4.0 5.0 5.0 7.0 5
## 1809 9.0 NA NA 7.0 8
## 1813 NA NA NA 5.0 7
## 1823 8.0 NA 7.0 9.0 10
## 1825 6.0 NA NA 7.0 7
## 1833 7.0 7.0 8.0 4.0 6
## 1834 5.0 6.0 NA 5.0 7
## 1835 6.0 7.0 NA 8.0 10
## 1836 4.0 5.0 5.0 6.0 8
## 1837 3.0 6.0 3.0 5.0 5
## 1838 6.0 7.0 7.0 6.0 8
## 1840 7.0 7.0 7.0 8.0 8
## 1841 7.0 NA NA 5.0 8
## 1842 2.0 5.0 3.0 5.0 7
## 1843 6.0 6.0 3.0 6.0 8
## 1844 7.0 8.0 7.0 8.0 8
## 1845 6.0 6.0 5.0 6.0 8
## 1846 6.0 7.0 7.0 8.0 8
## 1847 1.0 3.0 1.0 8.0 8
## 1848 5.0 5.0 4.0 6.0 8
## 1857 9.0 NA NA 8.0 8
## 1860 9.0 8.0 NA 8.0 7
## 1863 7.0 8.0 6.0 8.0 10
## 1866 5.0 5.0 NA 3.0 8
## 1871 NA NA NA 4.0 6
## 1873 8.0 8.0 NA 7.0 8
## 1876 NA 9.0 NA 8.0 8
## 1881 8.0 7.0 6.0 6.0 7
## 1883 6.0 8.0 NA 9.0 7
## 1885 7.0 7.0 5.0 4.0 8
## 1887 6.0 NA NA 3.0 7
## 1888 7.0 6.0 5.0 8.0 7
## 1889 7.0 NA NA 8.0 5
## 1891 6.0 6.0 6.0 4.0 7
## 1893 6.0 6.0 6.0 4.0 8
## 1894 6.0 9.0 10.0 6.0 8
## 1895 5.0 6.0 7.0 7.0 7
## 1896 8.0 7.0 8.0 5.0 8
## 1897 7.0 7.0 7.0 5.0 5
## 1898 8.0 8.0 5.0 5.0 8
## 1902 8.0 8.0 5.0 7.0 7
## 1903 9.0 NA 9.0 8.0 7
## 1904 8.0 8.0 7.0 9.0 8
## 1905 8.0 9.0 NA 6.0 5
## 1907 7.0 7.0 7.0 7.0 8
## 1910 7.0 8.0 5.0 6.0 7
## 1911 4.0 6.0 3.0 6.0 7
## 1912 7.0 7.0 5.0 6.0 8
## 1914 7.0 7.0 NA 3.0 7
## 1915 8.0 7.0 8.0 8.0 6
## 1919 9.0 NA NA 6.0 8
## 1921 8.0 NA NA 6.0 4
## 1924 9.0 8.0 7.0 7.0 8
## 1926 8.0 7.0 6.0 5.0 7
## 1930 5.0 NA 3.0 4.0 6
## 1933 5.0 6.0 4.0 5.0 6
## 1934 6.0 7.0 5.0 7.0 8
## 1935 NA NA NA 6.0 8
## 1937 5.0 NA NA 5.0 5
## 1938 6.0 5.0 3.0 7.0 9
## 1941 5.0 6.0 5.0 4.0 6
## 1942 5.0 7.0 4.0 6.0 7
## 1943 4.0 6.0 3.0 6.0 8
## 1945 6.0 7.0 6.0 6.0 6
## 1946 6.0 6.0 5.0 5.0 5
## 1947 6.0 7.0 2.0 5.0 6
## 1948 6.0 5.0 3.0 7.0 7
## 1951 6.0 NA 6.0 6.0 6
## 1952 6.0 7.0 5.0 5.0 5
## 1953 4.0 NA NA 4.0 4
## 1954 5.0 6.0 4.0 5.0 6
## 1955 6.0 6.0 5.0 5.0 6
## 1956 7.0 8.0 6.0 7.0 7
## 1957 6.0 5.0 7.0 5.0 5
## 1958 6.0 7.0 4.0 6.0 6
## 1959 2.0 NA 1.0 6.0 5
## 1960 5.0 5.0 5.0 5.0 5
## 1967 NA NA NA 7.0 9
## 1969 7.0 NA NA 9.0 9
## 1978 7.0 5.0 NA 1.0 4
## 1979 8.0 NA NA 7.0 7
## 1983 NA 7.0 5.0 6.0 8
## 1985 7.0 NA NA 8.0 7
## 1994 5.0 5.0 NA 3.0 10
## 2001 7.0 NA NA 7.0 6
## 2010 6.0 NA NA 2.0 6
## 2015 9.0 NA NA 5.0 6
## 2017 8.0 NA NA 6.0 7
## 2026 6.0 7.0 NA 6.0 8
## 2029 NA NA NA 8.0 9
## 2032 4.0 7.0 NA 9.0 9
## 2033 6.0 9.0 NA 10.0 8
## 2036 6.0 NA NA 7.0 9
## 2041 8.0 7.0 5.0 7.0 7
## 2042 5.0 NA 5.0 5.0 7
## 2045 8.0 NA NA 6.0 8
## 2047 2.0 7.0 1.0 7.0 6
## 2049 8.0 8.0 NA 8.0 9
## 2050 4.0 6.0 5.0 8.0 8
## 2051 4.0 NA NA 7.0 7
## 2052 8.0 NA 5.0 7.0 7
## 2054 3.0 6.0 2.0 7.0 9
## 2055 6.0 10.0 3.0 6.0 7
## 2056 6.0 5.0 4.0 6.0 10
## 2057 8.0 10.0 6.0 6.0 9
## 2061 8.0 8.0 NA 5.0 8
## 2064 8.0 8.0 6.0 6.0 9
## 2066 7.0 7.0 6.0 8.0 8
## 2068 6.0 6.0 NA 7.0 9
## 2070 7.0 6.0 6.0 7.0 8
## 2077 NA NA NA 5.0 6
## 2084 7.0 NA 6.0 7.0 9
## 2090 8.0 9.0 NA 3.0 6
## 2093 NA 8.0 NA 4.0 6
## 2096 4.0 8.0 NA 8.0 6
## 2099 7.0 NA 8.0 6.0 7
## 2109 8.0 NA NA 5.0 8
## 2113 8.0 6.0 NA 9.0 9
## 2121 8.0 5.0 6.0 8.0 7
## 2122 8.0 8.0 NA 4.0 9
## 2124 9.0 8.0 9.0 4.0 8
## 2127 6.0 7.0 2.0 7.0 7
## 2128 3.0 3.0 3.0 6.0 6
## 2129 7.0 6.0 7.0 9.0 10
## 2130 5.0 7.0 8.0 9.0 9
## 2131 7.0 NA 9.0 8.0 9
## 2132 7.0 NA 6.0 7.0 8
## 2133 9.0 7.0 5.0 8.0 7
## 2134 7.0 8.0 8.0 5.0 10
## 2136 7.0 7.0 6.0 8.0 7
## 2138 10.0 6.0 NA 6.0 9
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## 2145 7.0 8.0 NA 8.0 10
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## 2153 8.0 9.0 5.0 4.0 4
## 2154 8.0 8.0 NA 3.0 7
## 2155 7.0 7.0 6.0 7.0 7
## 2156 7.0 7.0 4.0 5.0 7
## 2157 6.0 NA NA 7.0 7
## 2158 6.0 8.0 7.0 8.0 8
## 2159 5.0 5.0 2.0 8.0 8
## 2160 3.0 7.0 8.0 7.0 7
## 2161 5.0 8.0 NA 5.0 7
## 2162 6.0 5.0 6.0 8.0 8
## 2163 5.0 NA 7.0 8.0 8
## 2164 4.0 NA NA 6.0 7
## 2165 9.0 9.0 7.0 7.0 7
## 2166 6.0 6.0 5.0 7.0 7
## 2167 9.0 10.0 6.0 7.0 7
## 2168 7.0 7.0 6.0 8.0 8
## 2170 6.0 4.0 NA 3.0 7
## 2173 7.0 NA NA 3.0 6
## 2180 7.0 NA 5.0 4.0 8
## 2186 9.0 9.0 NA 5.0 5
## 2189 NA NA NA 4.0 6
## 2192 4.0 7.0 NA 5.0 6
## 2193 8.0 8.0 NA 8.0 7
## 2196 6.0 NA NA 7.0 7
## 2205 8.0 NA NA 7.0 8
## 2206 7.0 7.0 5.0 10.0 8
## 2207 5.0 5.0 2.0 9.0 9
## 2210 7.0 7.0 NA 9.0 9
## 2212 7.0 NA 6.0 9.0 10
## 2219 6.0 7.0 5.0 NA NA
## 2221 NA NA NA 5.0 7
## 2224 4.0 8.0 NA 7.0 7
## 2227 6.0 8.0 NA 5.0 7
## 2228 5.0 NA 4.0 5.0 7
## 2237 7.0 NA NA 6.0 8
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## 2243 8.0 NA 8.0 7.0 7
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## 2250 8.0 8.0 NA NA 8
## 2253 7.0 NA NA 1.0 4
## 2254 10.0 8.0 8.0 8.0 4
## 2257 7.0 8.0 NA 4.0 6
## 2259 8.0 9.0 NA 7.0 8
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## 2273 8.0 8.0 NA 7.0 7
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## 2283 NA 5.0 NA 5.0 5
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## 2307 7.0 9.0 3.0 8.0 7
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## 2317 8.0 5.0 NA 6.0 7
## 2321 8.0 9.0 7.0 5.0 7
## 2322 6.0 8.0 9.0 7.0 7
## 2323 2.0 NA 5.0 5.0 7
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## 2325 5.0 5.0 5.0 5.0 6
## 2326 5.0 5.0 6.0 7.0 7
## 2327 9.0 NA 9.0 7.0 5
## 2328 7.0 5.0 5.0 8.0 7
## 2329 6.0 6.0 4.0 6.0 6
## 2330 6.0 10.0 9.0 7.0 8
## 2334 8.0 NA NA 4.0 8
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## 2347 7.0 NA NA 8.0 7
## 2353 NA 8.0 NA 8.0 8
## 2354 NA NA NA 4.0 4
## 2357 9.0 NA NA 5.0 5
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## 2368 7.0 8.0 5.0 9.0 9
## 2374 1.0 NA 1.0 6.0 6
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## 2391 5.0 5.0 NA 6.0 6
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## 2405 5.0 NA NA 5.0 6
## 2407 5.0 5.0 6.0 7.0 7
## 2408 7.0 9.0 7.0 6.0 8
## 2409 9.0 10.0 8.0 5.0 7
## 2411 5.0 5.0 7.0 6.0 8
## 2412 7.0 6.0 7.0 6.0 NA
## 2413 5.0 5.0 5.0 NA NA
## 2414 5.0 NA 4.0 4.0 NA
## 2415 5.0 NA NA NA NA
## 2416 6.0 6.0 7.0 7.0 7
## 2417 5.0 5.0 4.0 NA NA
## 2418 4.0 4.0 4.0 NA NA
## 2419 8.0 9.0 9.0 NA NA
## 2420 6.0 6.0 6.0 4.0 NA
## 2425 5.0 NA NA 5.0 5
## 2435 5.0 NA NA 5.0 8
## 2441 6.0 6.0 6.0 5.0 8
## 2443 8.0 6.0 2.0 9.0 NA
## 2444 8.0 8.0 3.0 5.0 NA
## 2445 7.0 5.0 NA 3.0 9
## 2447 8.0 7.0 6.0 7.0 5
## 2448 10.0 5.0 4.0 6.0 9
## 2449 9.0 9.0 8.0 7.0 5
## 2455 7.0 7.0 NA 5.0 8
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## 2475 NA NA NA 3.0 9
## 2486 3.0 4.0 5.0 8.0 9
## 2500 7.0 8.0 NA 6.0 7
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## 2523 7.0 7.0 6.0 2.0 3
## 2525 3.0 6.0 2.0 2.0 3
## 2527 9.0 9.0 10.0 4.0 4
## 2528 4.0 3.0 3.0 3.0 6
## 2530 2.0 8.0 2.0 2.0 7
## 2531 5.0 5.0 5.0 2.0 5
## 2535 4.0 5.0 6.0 2.0 5
## 2536 2.0 1.0 1.0 3.0 3
## 2537 5.0 5.0 6.0 2.0 3
## 2538 6.0 6.0 3.0 3.0 6
## 2539 5.0 5.0 4.0 3.0 5
## 2540 7.0 7.0 NA 2.0 8
## 2550 4.0 8.0 5.0 7.0 7
## 2560 8.0 8.0 NA 7.0 7
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## 2589 8.0 6.0 7.0 2.0 9
## 2600 9.0 8.0 NA 8.0 10
## 2601 7.0 6.0 5.0 7.0 10
## 2602 8.0 6.0 6.0 6.0 8
## 2603 8.0 8.0 5.0 3.0 7
## 2604 5.0 7.0 5.0 8.0 10
## 2605 6.0 6.0 6.0 6.0 7
## 2606 7.0 7.0 7.0 7.0 9
## 2607 8.0 9.0 10.0 6.0 10
## 2608 7.0 6.0 5.0 7.0 9
## 2609 6.0 8.0 4.0 3.0 9
## 2610 8.0 7.0 NA 6.0 10
## 2611 7.0 6.0 5.0 7.0 10
## 2612 6.0 9.0 8.0 7.0 9
## 2613 7.0 7.0 7.0 7.0 8
## 2614 7.0 8.0 8.0 7.0 8
## 2615 4.0 5.0 4.0 5.0 10
## 2616 6.0 6.0 3.0 7.0 10
## 2617 4.0 7.0 6.0 8.0 7
## 2618 8.0 6.0 8.0 5.0 7
## 2619 10.0 9.0 9.0 5.0 9
## 2620 7.0 8.0 NA NA NA
## 2621 6.0 8.0 6.0 7.0 7
## 2623 9.0 9.0 9.0 3.0 8
## 2624 5.0 4.0 5.0 7.0 8
## 2625 5.0 5.0 4.0 2.0 8
## 2626 5.0 5.0 6.0 7.0 NA
## 2628 8.0 8.0 5.0 7.0 7
## 2629 4.0 4.0 2.0 1.0 7
## 2630 8.0 6.0 7.0 6.0 8
## 2631 6.0 6.0 5.0 4.0 7
## 2632 7.0 8.0 6.0 5.0 8
## 2633 7.0 5.0 6.0 9.0 7
## 2634 6.0 7.0 6.0 6.0 7
## 2635 5.0 5.0 3.0 5.0 8
## 2636 5.0 5.0 5.0 7.0 7
## 2639 6.0 6.0 4.0 4.0 8
## 2640 8.0 8.0 NA 4.0 8
## 2660 8.0 9.0 NA 7.0 8
## 2661 8.0 7.0 6.0 3.0 6
## 2665 6.0 6.0 6.0 7.0 5
## 2666 7.0 6.0 7.0 8.0 6
## 2668 6.0 6.0 3.0 3.0 7
## 2670 8.0 8.0 7.0 5.0 8
## 2671 5.0 5.0 5.0 4.0 7
## 2672 6.0 8.0 7.0 6.0 9
## 2675 5.0 5.0 8.0 6.0 7
## 2679 8.0 7.0 5.0 5.0 6
## 2680 8.0 9.0 NA 6.0 10
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## 2720 7.0 7.0 NA 6.0 8
## 2726 5.0 5.0 6.0 7.0 7
## 2739 5.0 6.0 5.0 4.0 9
## 2740 7.0 7.0 NA 1.0 7
## 2751 7.0 5.0 5.0 4.0 8
## 2760 8.0 8.0 NA 9.0 8
## 2763 9.0 9.0 9.0 5.0 5
## 2765 8.0 7.0 4.0 5.0 8
## 2770 8.0 7.0 6.0 5.0 5
## 2780 8.0 8.0 NA 6.0 8
## 2781 7.0 8.0 6.0 6.0 5
## 2782 5.0 8.0 5.0 8.0 NA
## 2783 8.0 7.0 6.0 5.0 6
## 2784 5.0 5.0 5.0 6.0 6
## 2785 5.0 4.0 1.0 5.0 NA
## 2786 5.0 5.0 5.0 6.0 5
## 2787 9.0 9.0 9.0 5.0 7
## 2788 6.0 3.0 2.0 8.0 7
## 2789 7.0 6.0 3.0 3.0 NA
## 2790 4.0 6.0 4.0 5.0 7
## 2791 7.0 7.0 7.0 7.0 7
## 2792 8.0 7.0 7.0 5.0 6
## 2793 7.0 8.0 5.0 8.0 7
## 2794 5.0 4.0 4.0 8.0 8
## 2795 7.0 7.0 4.0 5.0 6
## 2796 5.0 6.0 3.0 8.0 NA
## 2797 5.0 6.0 4.0 5.0 5
## 2798 5.0 6.0 7.0 8.0 8
## 2799 6.0 7.0 5.0 5.0 NA
## 2800 8.0 8.0 NA 5.0 NA
## 2801 8.0 8.0 7.0 7.0 10
## 2803 9.0 9.0 8.0 1.0 1
## 2804 6.0 5.0 6.0 9.0 NA
## 2805 5.0 5.0 5.0 6.0 10
## 2806 4.0 5.0 5.0 9.0 8
## 2807 9.0 9.0 9.0 5.0 10
## 2808 6.0 6.0 4.0 5.0 8
## 2809 6.0 5.0 5.0 3.0 NA
## 2810 8.0 7.0 7.0 4.0 10
## 2811 3.0 5.0 5.0 7.0 10
## 2812 4.0 3.0 4.0 7.0 10
## 2813 7.0 7.0 7.0 10.0 10
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## 2816 5.0 6.0 3.0 10.0 9
## 2817 7.0 5.0 7.0 8.0 10
## 2818 6.0 6.0 5.0 5.0 2
## 2819 7.0 6.0 7.0 3.0 9
## 2820 8.0 8.0 NA 4.0 2
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## 2860 8.0 8.0 NA 6.0 7
## 2880 8.0 8.0 NA 8.0 8
## 2887 7.0 9.0 NA 9.0 5
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## 2897 5.0 NA 6.0 9.0 8
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## 2907 8.0 8.0 NA 8.0 7
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## 2928 7.0 NA NA 9.0 9
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## 2947 8.0 10.0 NA 7.0 7
## 2948 NA 9.0 NA 5.0 7
## 2956 NA 9.0 NA 4.0 7
## 2957 1.0 NA NA 4.0 7
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## 2966 4.0 9.0 4.0 4.0 4
## 2967 5.0 9.0 NA 7.0 6
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## 2976 NA NA NA 3.0 4
## 2977 NA NA NA 4.0 4
## 2981 7.0 8.0 6.0 3.0 7
## 2987 8.0 8.0 NA 7.0 7
## 2988 NA NA NA 5.0 8
## 2989 7.0 8.0 5.0 4.0 7
## 2990 8.0 8.0 NA 7.0 7
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## 2994 10.0 9.0 8.0 8.0 6
## 2995 9.0 9.0 9.0 7.0 7
## 2996 NA NA NA 5.0 7
## 2997 NA NA NA 4.0 7
## 3003 3.0 3.0 NA 10.0 10
## 3007 NA 8.0 NA 9.0 9
## 3016 8.0 NA NA 9.0 9
## 3017 NA 9.0 NA 9.0 9
## 3023 3.0 3.0 NA 6.0 5
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## 3030 NA 9.0 3.0 6.0 7
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## 3037 2.0 3.0 NA 5.0 7
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## 3048 1.0 NA NA 5.0 4
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## 3057 1.0 NA NA 7.0 6
## 3063 5.0 NA NA 7.0 8
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## 3068 NA 9.0 NA 8.0 9
## 3070 7.0 9.0 NA 9.0 9
## 3076 5.0 6.0 NA 8.0 9
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## 3088 NA NA NA 6.0 6
## 3090 3.0 8.0 NA 6.0 8
## 3096 NA 8.0 NA 5.0 8
## 3097 NA NA 6.0 3.0 3
## 3107 7.0 8.0 NA 7.0 7
## 3108 7.0 9.0 NA 5.0 7
## 3110 8.0 8.0 NA 6.0 5
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## 3128 NA 9.0 9.0 6.0 7
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## 3137 10.0 NA NA 6.0 9
## 3147 7.0 7.0 NA 8.0 7
## 3148 NA NA NA 6.0 8
## 3156 NA 8.0 5.0 4.0 7
## 3163 2.0 NA NA 6.0 7
## 3167 6.0 9.0 NA 4.0 7
## 3168 4.0 NA NA 6.0 7
## 3170 7.0 10.0 NA 6.0 7
## 3176 5.0 NA 5.0 6.0 7
## 3177 1.0 NA 1.0 6.0 6
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## 3187 9.0 NA 8.0 8.0 8
## 3188 7.0 7.0 NA 5.0 8
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## 3197 7.0 9.0 NA 6.0 8
## 3203 3.0 3.0 NA 8.0 7
## 3207 7.0 6.0 6.0 6.0 6
## 3216 5.0 7.0 NA 5.0 8
## 3217 10.0 NA NA 5.0 8
## 3223 8.0 NA 3.0 8.0 6
## 3227 7.0 7.0 NA 8.0 8
## 3235 7.0 8.0 7.0 8.0 8
## 3236 NA NA NA 6.0 6
## 3237 1.0 NA 1.0 6.0 7
## 3243 3.0 3.0 NA 6.0 6
## 3247 5.0 8.0 NA 8.0 8
## 3248 4.0 NA NA 6.0 6
## 3250 6.0 8.0 NA 7.0 8
## 3253 7.0 NA 7.0 6.0 7
## 3256 NA 7.0 NA 6.0 7
## 3257 1.0 NA 5.0 6.0 7
## 3261 7.0 8.0 6.0 6.0 7
## 3262 9.0 8.0 7.0 6.0 7
## 3263 3.0 NA 9.0 7.0 8
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## 4951 4.0 3.0 3.0 9.0 NA
## 4953 NA NA NA NA NA
## 4954 NA NA NA NA NA
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## 316 5.0 3.0 5.0
## 319 6.0 5.0 NA
## 320 6.0 7.0 6.0
## 323 7.0 7.0 7.0
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## 330 8.0 5.0 9.0
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## 353 8.0 8.0 8.0
## 354 8.0 6.0 7.0
## 355 8.0 9.0 8.0
## 361 7.0 9.0 NA
## 362 9.0 NA 9.0
## 363 6.0 7.0 8.0
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## 371 8.0 9.0 8.0
## 378 7.0 4.0 6.0
## 385 6.0 6.0 7.0
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## 443 6.0 7.0 5.0
## 448 7.0 6.0 6.0
## 451 6.0 8.0 4.0
## 456 6.0 7.0 4.0
## 458 10.0 1.0 10.0
## 459 10.0 7.0 7.0
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## 475 9.0 7.0 9.0
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## 488 8.0 10.0 7.0
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## 508 5.0 5.0 5.0
## 509 8.0 8.0 8.0
## 510 5.0 5.0 5.0
## 511 5.0 5.0 5.0
## 512 5.0 5.0 5.0
## 513 8.0 8.0 8.0
## 514 8.0 8.0 8.0
## 515 10.0 10.0 10.0
## 516 8.0 8.0 8.0
## 517 10.0 10.0 10.0
## 518 10.0 10.0 10.0
## 519 10.0 10.0 10.0
## 520 10.0 5.0 5.0
## 521 5.0 5.0 5.0
## 522 5.0 5.0 5.0
## 523 5.0 5.0 5.0
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## 527 9.0 8.0 9.0
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## 531 8.0 9.0 8.0
## 532 7.0 5.0 8.0
## 533 7.0 6.0 6.0
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## 541 9.0 8.0 10.0
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## 546 6.0 4.0 7.0
## 550 5.0 4.0 6.0
## 555 5.0 6.0 7.0
## 564 8.0 7.0 8.0
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## 582 6.0 6.0 5.0
## 600 8.0 8.0 7.0
## 604 6.0 8.0 7.0
## 607 8.0 7.0 10.0
## 609 7.0 9.0 8.0
## 614 7.0 9.0 7.0
## 616 6.0 5.0 6.0
## 622 5.0 6.0 7.0
## 630 8.0 4.0 6.0
## 634 6.0 5.0 NA
## 636 8.0 5.0 NA
## 637 7.0 8.0 NA
## 638 7.0 9.0 NA
## 639 5.0 6.0 NA
## 641 5.0 4.0 NA
## 642 7.0 8.0 NA
## 644 8.0 5.0 NA
## 649 7.0 8.0 NA
## 652 7.0 7.0 7.0
## 654 6.0 5.0 6.0
## 657 7.0 7.0 7.0
## 659 7.0 7.0 7.0
## 660 8.0 8.0 9.0
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## 669 8.0 NA NA
## 670 7.0 6.0 NA
## 671 7.0 6.0 NA
## 672 NA 9.0 NA
## 673 NA NA NA
## 674 NA NA NA
## 675 NA 7.0 NA
## 676 NA NA NA
## 677 NA NA NA
## 678 8.0 8.0 8.0
## 679 NA NA NA
## 681 NA NA 9.0
## 682 NA NA NA
## 683 NA NA NA
## 684 NA NA NA
## 685 NA NA NA
## 686 NA NA NA
## 690 7.0 6.0 6.0
## 704 8.0 8.0 8.0
## 708 9.0 5.0 6.0
## 720 6.0 7.0 6.0
## 722 6.0 6.0 7.0
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## 726 8.0 8.0 7.0
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## 779 5.0 3.0 5.0
## 780 8.0 5.0 8.0
## 790 7.0 8.0 9.0
## 797 10.0 5.0 NA
## 798 10.0 3.0 NA
## 800 10.0 9.0 NA
## 804 10.0 9.0 NA
## 806 10.0 5.0 NA
## 807 7.0 8.0 8.0
## 820 8.0 8.0 8.0
## 822 8.0 7.0 8.0
## 823 6.0 6.0 7.0
## 830 8.0 5.0 6.0
## 832 5.0 3.0 5.0
## 844 7.0 6.0 5.0
## 859 7.0 8.0 8.0
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## 867 5.0 10.0 7.0
## 881 5.0 7.0 6.0
## 882 7.0 5.0 5.0
## 883 7.0 4.0 4.0
## 885 8.0 6.0 7.0
## 886 8.0 7.0 9.0
## 901 8.0 10.0 6.0
## 902 6.0 7.0 NA
## 915 6.0 6.0 7.0
## 916 7.0 3.0 6.0
## 931 7.0 6.0 5.0
## 937 5.0 10.0 8.0
## 938 5.0 4.0 6.0
## 939 4.0 4.0 4.0
## 940 10.0 8.0 NA
## 941 5.0 2.0 NA
## 942 8.0 6.0 8.0
## 943 NA NA NA
## 944 5.0 NA NA
## 945 8.0 1.0 NA
## 946 5.0 NA 10.0
## 947 5.0 5.0 NA
## 948 8.0 8.0 NA
## 949 8.0 6.0 6.0
## 950 7.0 10.0 NA
## 952 6.0 10.0 7.0
## 953 6.0 7.0 6.0
## 954 8.0 NA NA
## 957 7.0 4.0 6.0
## 959 9.0 6.0 7.0
## 961 6.0 4.0 NA
## 962 6.0 3.0 NA
## 967 7.0 5.0 6.0
## 975 9.0 2.0 9.0
## 976 9.0 NA NA
## 977 9.0 2.0 9.0
## 978 9.0 9.0 NA
## 979 9.0 4.0 NA
## 980 9.0 2.0 9.0
## 981 8.0 NA NA
## 982 9.0 NA NA
## 985 9.0 NA NA
## 989 5.0 2.0 NA
## 990 9.0 NA 9.0
## 995 2.0 2.0 2.0
## 998 6.0 5.0 5.0
## 1001 7.0 8.0 6.0
## 1007 4.0 4.0 4.0
## 1009 8.0 7.0 NA
## 1011 8.0 7.0 8.0
## 1012 8.0 8.0 NA
## 1013 7.0 6.0 6.0
## 1016 7.0 6.0 8.0
## 1021 8.0 8.0 8.0
## 1025 8.0 8.0 8.0
## 1029 8.0 4.0 7.0
## 1031 6.0 4.0 6.0
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## 1047 8.0 8.0 9.0
## 1049 7.0 7.0 8.0
## 1057 9.0 8.0 9.0
## 1065 8.0 5.0 7.0
## 1067 7.0 4.0 5.0
## 1070 8.0 7.0 8.0
## 1079 8.0 4.0 8.0
## 1085 5.0 2.0 2.0
## 1088 7.0 2.0 2.0
## 1097 5.0 4.0 9.0
## 1101 6.0 6.0 6.0
## 1103 8.0 1.0 1.0
## 1106 9.0 4.0 8.0
## 1111 7.0 6.0 8.0
## 1115 5.0 7.0 5.0
## 1117 8.0 9.0 5.0
## 1119 7.0 4.0 NA
## 1121 7.0 5.0 5.0
## 1124 7.0 5.0 7.0
## 1134 5.0 5.0 5.0
## 1137 6.0 5.0 7.0
## 1139 8.0 5.0 9.0
## 1142 4.0 2.0 4.0
## 1147 5.0 4.0 7.0
## 1151 6.0 7.0 5.0
## 1155 9.0 2.0 10.0
## 1157 10.0 3.0 10.0
## 1160 10.0 3.0 10.0
## 1173 9.0 7.0 8.0
## 1175 7.0 6.0 7.0
## 1183 10.0 9.0 10.0
## 1191 6.0 5.0 6.0
## 1193 6.0 5.0 4.0
## 1201 7.0 8.0 7.0
## 1205 6.0 7.0 8.0
## 1207 7.0 7.0 8.0
## 1208 6.0 6.0 7.0
## 1210 8.0 6.0 7.0
## 1211 8.0 5.0 8.0
## 1212 8.0 8.0 8.0
## 1213 8.0 7.0 7.0
## 1214 8.0 5.0 7.0
## 1216 8.0 7.0 7.0
## 1217 7.0 7.0 7.0
## 1218 10.0 10.0 10.0
## 1219 8.0 7.0 7.0
## 1220 8.0 8.0 NA
## 1222 8.0 8.0 8.0
## 1223 8.0 8.0 8.0
## 1224 8.0 7.0 7.0
## 1227 10.0 6.0 9.0
## 1229 8.0 3.0 8.0
## 1232 10.0 8.0 9.0
## 1237 8.0 8.0 8.0
## 1241 9.0 8.0 8.0
## 1245 7.0 6.0 6.0
## 1246 8.0 8.0 NA
## 1247 6.0 6.0 6.0
## 1248 7.0 7.0 7.0
## 1250 7.0 6.0 7.0
## 1261 6.0 7.0 7.0
## 1265 7.0 7.0 7.0
## 1271 8.0 8.0 6.0
## 1276 8.0 8.0 8.0
## 1279 7.0 8.0 4.0
## 1294 8.0 7.0 8.0
## 1297 7.0 8.0 NA
## 1298 7.0 6.0 NA
## 1299 6.0 6.0 6.0
## 1301 7.0 6.0 NA
## 1302 7.0 8.0 NA
## 1303 8.0 8.0 NA
## 1304 7.0 6.0 6.0
## 1306 7.0 7.0 NA
## 1307 9.0 8.0 NA
## 1308 6.0 7.0 NA
## 1309 7.0 7.0 NA
## 1310 7.0 7.0 7.0
## 1311 7.0 6.0 6.0
## 1313 7.0 6.0 NA
## 1314 8.0 8.0 NA
## 1315 7.0 7.0 7.0
## 1317 8.0 7.0 6.0
## 1319 7.0 4.0 6.0
## 1322 7.0 6.0 8.0
## 1327 6.0 5.0 7.0
## 1330 8.0 8.0 9.0
## 1332 8.0 8.0 7.0
## 1333 10.0 NA NA
## 1334 10.0 NA NA
## 1335 10.0 NA NA
## 1336 10.0 NA NA
## 1337 10.0 NA NA
## 1338 10.0 NA NA
## 1339 10.0 NA NA
## 1340 10.0 NA NA
## 1341 10.0 NA NA
## 1342 10.0 NA NA
## 1343 10.0 10.0 NA
## 1344 10.0 NA NA
## 1345 10.0 10.0 NA
## 1346 10.0 NA NA
## 1347 10.0 NA 10.0
## 1348 10.0 NA NA
## 1349 10.0 NA NA
## 1350 10.0 NA NA
## 1351 8.0 5.0 7.0
## 1353 3.0 4.0 2.0
## 1365 8.0 6.0 5.0
## 1366 8.0 7.0 8.0
## 1368 7.0 5.0 9.0
## 1369 8.0 7.0 7.0
## 1370 6.0 5.0 5.0
## 1371 7.0 7.0 8.0
## 1384 9.0 9.0 9.0
## 1387 8.0 8.0 NA
## 1388 6.0 5.0 NA
## 1389 6.0 5.0 NA
## 1390 8.0 6.0 NA
## 1391 7.0 NA NA
## 1392 7.0 7.0 NA
## 1394 8.0 7.0 NA
## 1395 8.0 7.0 NA
## 1396 7.0 8.0 NA
## 1397 8.0 9.0 NA
## 1398 6.0 6.0 NA
## 1399 8.0 7.0 NA
## 1402 7.0 8.0 NA
## 1403 8.0 7.0 NA
## 1404 8.0 8.0 NA
## 1405 8.0 10.0 6.0
## 1407 6.0 3.0 6.0
## 1423 9.0 7.0 7.0
## 1425 6.0 6.0 6.0
## 1438 8.0 8.0 8.0
## 1441 7.0 6.0 6.0
## 1444 7.0 7.0 7.0
## 1456 7.0 6.0 8.0
## 1459 8.0 9.0 8.0
## 1477 7.0 8.0 4.0
## 1478 7.0 6.0 4.0
## 1479 10.0 8.0 8.0
## 1481 7.0 3.0 10.0
## 1483 5.0 5.0 3.0
## 1486 6.0 3.0 6.0
## 1488 6.0 8.0 4.0
## 1490 8.0 8.0 10.0
## 1491 8.0 3.0 NA
## 1492 10.0 10.0 10.0
## 1493 4.0 4.0 6.0
## 1495 9.0 9.0 9.0
## 1510 8.0 8.0 8.0
## 1531 8.0 8.0 8.0
## 1546 8.0 7.0 8.0
## 1549 8.0 8.0 7.0
## 1551 7.0 4.0 6.0
## 1552 9.0 8.0 6.0
## 1553 8.0 6.0 9.0
## 1556 9.0 8.0 8.0
## 1557 7.0 6.0 8.0
## 1558 8.0 8.0 8.0
## 1560 8.0 7.0 9.0
## 1563 8.0 4.0 NA
## 1564 8.0 7.0 8.0
## 1565 8.0 8.0 7.0
## 1567 8.0 8.0 6.0
## 1569 5.0 2.0 2.0
## 1577 7.0 4.0 7.0
## 1582 8.0 5.0 6.0
## 1616 NA NA NA
## 1626 8.0 7.0 8.0
## 1627 7.0 5.0 9.0
## 1632 NA NA NA
## 1633 5.0 5.0 5.0
## 1634 7.0 8.0 9.0
## 1635 8.0 NA 8.0
## 1636 8.0 9.0 8.0
## 1637 8.0 7.0 6.0
## 1639 NA NA NA
## 1640 6.0 2.0 NA
## 1663 8.0 7.0 8.0
## 1672 5.0 6.0 7.0
## 1681 4.0 5.0 3.0
## 1699 8.0 9.0 7.0
## 1704 7.0 2.0 5.0
## 1717 9.0 7.0 8.0
## 1723 NA NA NA
## 1725 NA NA NA
## 1726 6.0 6.0 6.0
## 1728 10.0 6.0 7.0
## 1731 9.0 5.0 9.0
## 1736 5.0 6.0 6.0
## 1741 8.0 5.0 8.0
## 1742 9.0 9.0 10.0
## 1744 NA NA NA
## 1745 8.0 NA NA
## 1746 NA 8.0 4.0
## 1747 8.0 8.0 NA
## 1748 NA 8.0 8.0
## 1752 8.0 7.0 7.0
## 1756 6.0 1.0 7.0
## 1757 8.0 8.0 7.0
## 1758 10.0 6.0 8.0
## 1759 9.0 7.0 6.0
## 1760 NA 6.0 5.0
## 1763 7.5 7.0 7.0
## 1764 8.0 7.0 7.0
## 1768 8.0 8.0 6.0
## 1770 9.0 8.0 7.0
## 1771 8.0 8.0 10.0
## 1775 10.0 8.0 5.0
## 1777 6.0 8.0 9.0
## 1785 8.0 6.0 7.0
## 1786 8.0 5.0 NA
## 1789 9.0 8.0 9.0
## 1791 9.0 8.0 8.0
## 1792 9.0 10.0 6.0
## 1793 9.0 8.0 8.0
## 1794 7.0 6.0 4.0
## 1795 9.0 9.0 9.0
## 1799 9.0 8.0 8.0
## 1800 8.0 9.0 8.0
## 1809 8.0 7.0 7.0
## 1813 8.0 6.0 7.0
## 1823 10.0 9.0 8.0
## 1825 7.0 7.0 7.0
## 1833 8.0 NA NA
## 1834 10.0 8.0 NA
## 1835 7.0 8.0 8.0
## 1836 8.0 NA NA
## 1837 10.0 NA 8.0
## 1838 8.0 8.0 NA
## 1840 7.0 NA NA
## 1841 6.0 6.0 NA
## 1842 8.0 7.0 NA
## 1843 10.0 NA NA
## 1844 8.0 8.0 NA
## 1845 8.0 NA NA
## 1846 8.0 7.0 NA
## 1847 8.0 7.0 NA
## 1848 8.0 8.0 NA
## 1857 8.0 6.0 8.0
## 1860 8.0 7.0 7.0
## 1863 10.0 10.0 8.0
## 1866 8.0 2.0 7.0
## 1871 7.0 6.0 7.0
## 1873 7.0 5.0 5.0
## 1876 8.0 5.0 5.0
## 1881 7.0 4.0 7.0
## 1883 8.0 8.0 8.0
## 1885 7.0 4.0 8.0
## 1887 8.0 3.0 3.0
## 1888 6.0 4.0 4.0
## 1889 6.0 3.0 7.0
## 1891 8.0 4.0 7.0
## 1893 7.0 4.0 8.0
## 1894 8.0 6.0 7.0
## 1895 7.0 6.0 6.0
## 1896 8.0 7.0 4.0
## 1897 8.0 6.0 9.0
## 1898 10.0 8.0 8.0
## 1902 8.0 8.0 6.0
## 1903 6.0 7.0 6.0
## 1904 8.0 7.0 8.0
## 1905 6.0 5.0 8.0
## 1907 8.0 8.0 8.0
## 1910 9.0 6.0 9.0
## 1911 8.0 7.0 8.0
## 1912 8.0 8.0 8.0
## 1914 7.0 4.0 6.0
## 1915 8.0 7.0 7.0
## 1919 8.0 5.0 7.0
## 1921 6.0 6.0 5.0
## 1924 7.0 7.0 7.0
## 1926 6.0 5.0 7.0
## 1930 5.0 4.0 NA
## 1933 8.0 7.0 NA
## 1934 7.0 7.0 6.0
## 1935 8.0 7.0 NA
## 1937 6.0 5.0 NA
## 1938 8.0 8.0 8.0
## 1941 7.0 6.0 8.0
## 1942 8.0 8.0 NA
## 1943 7.0 8.0 9.0
## 1945 8.0 6.0 NA
## 1946 6.0 8.0 NA
## 1947 6.0 6.0 6.0
## 1948 7.0 7.0 NA
## 1951 7.0 7.0 NA
## 1952 5.0 5.0 NA
## 1953 5.0 4.0 NA
## 1954 7.0 7.0 NA
## 1955 6.0 6.0 NA
## 1956 7.0 7.0 NA
## 1957 7.0 5.0 NA
## 1958 7.0 7.0 7.0
## 1959 6.0 6.0 7.0
## 1960 6.0 6.0 NA
## 1967 9.0 9.0 7.0
## 1969 9.0 9.0 9.0
## 1978 6.0 3.0 6.0
## 1979 6.0 7.0 6.0
## 1983 7.0 7.0 8.0
## 1985 7.0 6.0 6.0
## 1994 10.0 6.0 10.0
## 2001 10.0 9.0 10.0
## 2010 6.0 6.0 5.0
## 2015 6.0 7.0 7.0
## 2017 7.0 7.0 7.0
## 2026 9.0 6.0 8.0
## 2029 8.0 7.0 7.0
## 2032 9.0 8.0 7.0
## 2033 9.0 7.0 7.0
## 2036 8.0 6.0 7.0
## 2041 7.0 6.0 NA
## 2042 7.0 5.0 4.0
## 2045 8.0 5.0 6.0
## 2047 6.0 5.0 5.0
## 2049 9.0 8.0 8.0
## 2050 7.0 7.0 7.0
## 2051 7.0 5.0 NA
## 2052 7.0 6.0 6.0
## 2054 8.0 7.0 5.0
## 2055 7.0 5.0 5.0
## 2056 6.0 6.0 NA
## 2057 8.0 7.0 9.0
## 2061 NA 6.0 7.0
## 2064 9.0 6.0 8.0
## 2066 NA 8.0 7.0
## 2068 8.0 6.0 7.0
## 2070 8.0 8.0 NA
## 2077 7.0 4.0 5.0
## 2084 6.0 6.0 5.0
## 2090 6.0 2.0 7.0
## 2093 8.0 3.0 6.0
## 2096 6.0 7.0 7.0
## 2099 7.0 5.0 6.0
## 2109 7.0 6.0 7.0
## 2113 9.0 8.0 8.0
## 2121 9.0 7.0 NA
## 2122 9.0 5.0 NA
## 2124 9.0 8.0 NA
## 2127 7.0 NA NA
## 2128 6.0 6.0 NA
## 2129 9.0 9.0 NA
## 2130 7.0 9.0 NA
## 2131 9.0 NA NA
## 2132 8.0 6.0 NA
## 2133 7.0 NA NA
## 2134 8.0 NA 7.0
## 2136 8.0 9.0 NA
## 2138 9.0 6.0 8.0
## 2141 9.0 7.0 7.0
## 2144 9.0 7.0 6.0
## 2145 9.0 8.0 8.0
## 2148 8.0 6.0 7.0
## 2153 8.0 4.0 NA
## 2154 7.0 5.0 NA
## 2155 9.0 9.0 NA
## 2156 7.0 6.0 NA
## 2157 7.0 7.0 NA
## 2158 8.0 9.0 NA
## 2159 8.0 8.0 8.0
## 2160 7.0 7.0 NA
## 2161 9.0 8.0 9.0
## 2162 8.0 8.0 NA
## 2163 NA 5.0 NA
## 2164 7.0 4.0 NA
## 2165 7.0 7.0 NA
## 2166 7.0 7.0 NA
## 2167 7.0 7.0 NA
## 2168 8.0 8.0 NA
## 2170 6.0 4.0 5.0
## 2173 8.0 2.0 5.0
## 2180 6.0 5.0 6.0
## 2186 5.0 6.0 6.0
## 2189 7.0 6.0 6.0
## 2192 6.0 6.0 6.0
## 2193 7.0 7.0 7.0
## 2196 7.0 6.0 6.0
## 2205 9.0 7.0 8.0
## 2206 8.0 9.0 8.0
## 2207 9.0 NA 9.0
## 2210 9.0 9.0 8.0
## 2212 8.0 7.0 8.0
## 2219 NA NA NA
## 2221 7.0 6.0 6.0
## 2224 7.0 6.0 6.0
## 2227 6.0 5.0 6.0
## 2228 7.0 6.0 5.0
## 2237 9.0 6.0 7.0
## 2240 8.0 6.0 9.0
## 2241 9.0 7.0 8.0
## 2242 8.0 8.0 7.0
## 2243 8.0 5.0 7.0
## 2244 8.0 6.0 7.0
## 2250 9.0 2.0 8.0
## 2253 5.0 1.0 3.0
## 2254 NA 7.0 8.0
## 2257 8.0 4.0 6.0
## 2259 8.0 4.0 6.0
## 2260 8.0 2.0 NA
## 2266 5.0 4.0 5.0
## 2269 5.0 5.0 5.0
## 2272 8.0 8.0 7.0
## 2273 7.0 7.0 7.0
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## 6162 5.0 8.0 8.0
## 6163 9.0 9.0 8.0
## 6164 7.0 7.0 8.0
## 6169 8.0 9.0 8.0
## 6175 6.0 6.0 6.0
## 6177 7.0 7.0 6.0
## 6178 8.0 7.0 7.0
## 6181 6.0 7.0 6.0
## 6182 7.0 5.0 6.0
## 6189 9.0 4.0 9.0
## 6190 10.0 9.0 9.0
## 6193 10.0 9.0 9.0
## 6194 10.0 10.0 10.0
## 6195 10.0 10.0 10.0
## 6196 10.0 10.0 10.0
## 6200 5.0 4.0 3.0
## 6211 5.0 5.0 5.0
## 6214 5.0 5.0 5.0
## 6217 5.0 5.0 5.0
## 6218 5.0 5.0 5.0
## 6221 5.0 5.0 5.0
## 6223 5.0 5.0 5.0
## 6231 8.0 7.0 8.0
## 6239 8.0 5.0 8.0
## 6247 9.0 7.0 9.0
## 6250 6.0 5.0 8.0
## 6256 6.0 3.0 9.0
## 6265 8.0 8.0 9.0
## 6266 9.0 6.0 9.0
## 6267 7.0 6.0 6.0
## 6268 8.0 5.0 9.0
## 6272 8.0 7.0 8.0
## 6278 8.0 7.0 NA
## 6284 6.0 7.0 7.0
## 6290 7.0 7.0 6.0
## 6296 8.0 7.0 7.0
## 6309 8.0 7.0 7.0
## 6310 7.0 6.0 7.0
## 6311 7.0 6.0 NA
## 6318 NA NA NA
## 6320 9.0 7.0 7.0
## 6325 8.0 7.0 8.0
## 6353 10.0 10.0 10.0
## 6381 7.0 7.0 7.0
## 6394 9.0 8.0 9.0
## 6395 8.0 8.0 8.0
## 6396 8.0 7.0 7.0
## 6399 10.0 10.0 9.0
## 6402 8.0 4.0 7.0
## 6404 NA NA NA
## 6415 8.0 8.0 8.0
## 6420 9.0 8.0 9.0
## 6423 9.0 8.0 9.0
## 6436 6.0 5.0 10.0
## 6437 6.0 4.0 10.0
## 6441 4.0 5.0 10.0
## 6445 8.0 6.0 10.0
## 6447 9.0 6.0 9.0
## 6448 7.0 8.0 9.0
## 6450 9.0 NA 7.0
## 6452 7.0 6.0 7.0
## 6453 9.0 5.0 9.0
## 6455 10.0 9.0 10.0
## 6456 9.0 4.0 9.0
## 6459 10.0 7.0 10.0
## 6470 8.0 5.0 7.0
## 6480 9.0 6.0 7.0
## 6496 5.0 7.0 7.0
## 6497 7.0 8.0 8.0
## 6500 8.0 3.0 3.0
## 6501 7.0 6.0 6.0
## 6503 8.0 7.0 7.0
## 6505 7.0 7.0 6.0
## 6506 5.0 9.0 6.0
## 6508 5.0 4.0 6.0
## 6509 7.0 8.0 7.0
## 6511 3.0 3.0 3.0
## 6512 8.0 4.0 9.0
## 6513 8.0 8.0 6.0
## 6514 9.0 8.0 7.0
## 6515 7.0 4.0 3.0
## 6516 7.0 8.0 7.0
## 6517 4.0 8.0 8.0
## 6520 6.0 7.0 7.0
## 6530 8.0 6.0 5.0
## 6546 8.0 6.0 6.0
## 6549 NA 6.0 NA
## 6550 7.0 8.0 6.0
## 6560 7.0 7.0 5.0
## 6567 9.0 7.0 9.0
## 6576 3.0 3.0 3.0
## 6590 7.0 6.0 6.0
## 6596 4.0 4.0 4.0
## 6606 7.0 7.0 5.0
## 6612 8.0 6.0 NA
## 6618 5.0 4.0 4.0
## 6624 5.0 10.0 7.0
## 6630 3.0 4.0 1.0
## 6636 5.0 5.0 2.0
## 6655 4.0 6.0 5.0
## 6656 6.0 6.0 5.0
## 6658 5.0 6.0 6.0
## 6659 7.0 4.0 7.0
## 6660 7.0 6.0 7.0
## 6667 NA NA NA
## 6668 NA NA NA
## 6669 NA NA 8.0
## 6670 NA NA NA
## 6671 NA NA 9.0
## 6672 NA NA 8.0
## 6674 4.0 2.0 NA
## 6676 7.0 0.0 8.0
## 6678 NA 2.0 9.0
## 6679 5.0 1.0 NA
## 6685 8.0 7.0 6.0
## 6691 8.0 6.0 7.0
## 6700 8.0 10.0 9.0
## 6706 8.0 5.0 8.0
## 6718 7.0 5.0 7.0
## 6719 6.0 7.0 NA
## 6720 6.0 8.0 6.0
## 6721 6.0 5.0 7.0
## 6727 6.0 7.0 NA
## 6731 8.0 7.0 NA
## 6759 7.0 6.0 6.0
## 6760 6.0 6.0 6.0
## 6766 10.0 7.0 10.0
## 6777 6.0 NA 6.0
## 6793 8.0 6.0 7.0
## 6796 9.0 3.0 10.0
## 6798 9.0 6.0 10.0
## 6803 8.0 7.0 7.0
## 6807 8.0 10.0 6.0
## 6809 7.0 6.0 NA
## 6811 7.0 3.0 5.0
## 6814 7.0 3.0 3.0
## 6816 6.0 6.0 NA
## 6826 9.0 6.0 5.0
## 6830 6.0 5.0 4.0
## 6832 8.0 6.0 7.0
## 6834 7.0 7.0 5.0
## 6835 NA NA NA
## 6841 8.0 6.0 7.0
## 6842 6.0 4.0 NA
## 6844 8.0 6.0 6.0
## 6845 8.0 7.0 9.0
## 6846 8.0 8.0 7.0
## 6849 8.0 8.0 8.0
## 6850 6.0 7.0 7.0
## 6851 7.0 7.0 7.0
## 6852 8.0 8.0 7.0
## 6853 8.0 7.0 7.0
## 6854 6.0 6.0 9.0
## 6855 7.0 7.0 7.0
## 6856 9.0 5.0 7.0
## 6857 5.0 5.0 5.0
## 6858 8.0 6.0 7.0
## 6859 5.0 5.0 5.0
## 6860 5.0 7.0 7.0
## 6861 7.0 7.0 7.0
## 6862 NA NA NA
## 6863 9.0 9.0 9.0
## 6864 9.0 9.0 8.0
## 6865 9.0 9.0 10.0
## 6866 9.0 8.0 8.0
## 6867 5.0 4.0 4.0
## 6871 8.0 2.0 6.0
## 6884 7.0 7.0 8.0
## 6885 8.0 10.0 7.0
## 6886 NA NA NA
## 6887 7.0 6.0 6.0
## 6893 7.0 7.0 7.0
## 6894 9.0 9.0 8.0
## 6895 8.0 7.0 NA
## 6896 9.0 8.0 6.0
## 6897 7.0 6.0 6.0
## 6901 7.0 7.0 6.0
## 6913 7.0 6.0 8.0
## 6916 6.0 5.0 5.0
## 6922 7.0 7.0 5.0
## 6927 NA NA NA
## 6931 10.0 10.0 10.0
## 6943 10.0 10.0 NA
## 6944 8.0 7.0 NA
## 6945 9.0 8.0 9.0
## 6946 9.0 8.0 NA
## 6949 9.0 8.0 9.0
## 6951 9.0 9.0 NA
## 6952 7.0 7.0 NA
## 6953 7.0 8.0 NA
## 6954 9.0 7.0 NA
## 6955 10.0 7.0 10.0
## 6956 8.0 6.0 NA
## 6957 10.0 10.0 10.0
## 6969 10.0 10.0 9.0
## 6973 7.0 9.0 6.0
## 6975 7.0 8.0 8.0
## 6977 8.0 7.0 9.0
## 6981 6.0 9.0 9.0
## 6988 8.0 5.0 7.0
## 6997 8.0 7.0 8.0
## 6999 8.0 6.0 NA
## 7013 NA NA NA
## 7014 5.0 5.0 5.0
## 7027 6.0 10.0 6.0
## 7029 10.0 6.0 9.0
## 7031 8.0 5.0 8.0
## 7034 8.0 7.0 10.0
## 7036 8.0 6.0 5.0
## 7043 8.0 8.0 7.0
## 7045 8.0 9.0 9.0
## 7062 6.0 6.0 6.0
## 7068 7.0 7.0 NA
## 7073 10.0 10.0 10.0
## 7074 10.0 5.0 5.0
## 7076 10.0 1.0 10.0
## 7079 9.0 8.0 NA
## 7080 10.0 9.0 9.0
## 7083 10.0 7.0 9.0
## 7085 10.0 6.0 7.0
## 7088 10.0 9.0 10.0
## 7089 10.0 8.0 7.0
## 7092 9.0 9.0 7.0
## 7096 8.0 8.0 7.0
## 7099 7.0 7.0 7.0
## 7104 8.0 7.0 7.0
## 7105 8.0 6.0 8.0
## 7108 6.0 7.0 5.0
## 7114 6.0 7.0 5.0
## 7116 7.0 7.0 6.0
## 7119 6.0 6.0 6.0
## 7123 7.0 5.0 7.0
## 7126 8.0 8.0 NA
## 7127 8.0 6.0 8.0
## 7128 8.0 8.0 7.0
## 7137 3.0 10.0 8.0
## 7151 6.0 6.0 7.0
## 7158 7.0 7.0 6.0
## 7165 8.0 7.0 NA
## 7167 8.0 8.0 NA
## 7169 7.0 NA NA
## 7170 9.0 8.0 NA
## 7183 8.0 7.0 7.0
## 7189 7.0 5.0 NA
## 7195 9.0 NA 8.0
## 7208 NA NA NA
## 7209 2.0 2.0 3.0
## 7217 NA NA NA
## 7219 NA NA NA
## 7220 6.0 5.0 5.0
## 7228 NA 8.0 8.0
## 7232 9.0 4.0 4.0
## 7235 NA NA NA
## 7236 8.0 8.0 NA
## 7237 7.0 7.0 7.0
## 7241 7.0 7.0 6.0
## 7242 7.0 4.0 7.0
## 7243 NA NA NA
## 7245 6.0 4.0 6.0
## 7247 10.0 9.0 NA
## 7249 5.0 5.0 8.0
## 7250 9.0 8.0 7.0
## 7253 7.0 6.0 6.0
## 7265 NA NA NA
## 7274 8.0 2.0 5.0
## 7280 4.0 4.0 NA
## 7281 8.0 8.0 6.0
## 7283 NA NA NA
## 7302 7.0 NA 10.0
## 7309 NA NA NA
## 7319 7.0 5.0 6.0
## 7320 6.0 7.0 6.0
## 7322 6.0 8.0 7.0
## 7323 8.0 8.0 8.0
## 7328 8.0 6.0 8.0
## 7329 7.0 8.0 7.0
## 7331 NA NA NA
## 7332 7.0 8.0 8.0
## 7333 8.0 7.0 6.0
## 7335 7.0 6.0 6.0
## 7336 3.0 5.0 5.0
## 7337 6.0 5.0 4.0
## 7338 8.0 7.0 7.0
## 7341 7.0 7.0 6.0
## 7353 NA NA NA
## 7363 NA NA NA
## 7366 NA NA NA
## 7369 NA NA NA
## 7372 NA NA NA
## 7374 NA NA NA
## 7379 NA NA NA
## 7383 7.0 4.0 8.0
## 7384 6.0 5.0 6.0
## 7385 7.0 6.0 6.0
## 7386 6.0 4.0 6.0
## 7387 7.0 7.0 NA
## 7388 7.0 7.0 6.0
## 7389 6.0 5.0 NA
## 7390 5.0 6.0 NA
## 7391 7.0 4.0 NA
## 7392 9.0 7.0 NA
## 7393 5.0 5.0 5.0
## 7395 7.0 6.0 NA
## 7398 7.0 7.0 6.0
## 7399 9.0 9.0 7.0
## 7400 8.0 6.0 7.0
## 7403 6.0 5.0 5.0
## 7407 5.0 2.0 1.0
## 7410 6.0 NA NA
## 7411 6.0 NA NA
## 7412 6.0 NA NA
## 7414 NA NA NA
## 7416 3.0 2.0 1.0
## 7417 NA NA NA
## 7425 NA NA NA
## 7426 NA NA NA
## 7429 5.0 6.0 6.0
## 7432 NA NA NA
## 7433 8.0 8.0 6.0
## 7434 NA NA NA
## 7442 NA NA NA
## 7451 8.0 8.0 8.0
## 7462 NA NA NA
## 7464 8.0 9.0 8.0
## 7467 NA NA NA
## 7478 7.0 9.0 NA
## 7482 8.0 7.0 NA
## 7484 8.0 8.0 NA
## 7488 5.0 4.0 NA
## 7489 9.0 8.0 NA
## 7495 7.0 5.0 7.0
## 7496 7.0 5.0 7.0
## 7500 6.0 8.0 2.0
## 7504 7.0 8.0 NA
## 7510 7.0 7.0 7.0
## 7517 7.0 7.0 6.0
## 7529 NA NA NA
## 7536 7.0 7.0 6.0
## 7538 8.0 4.0 9.0
## 7539 7.0 4.0 8.0
## 7551 NA NA NA
## 7558 7.0 7.0 8.0
## 7560 7.0 1.0 1.0
## 7561 6.0 2.0 5.0
## 7562 3.0 7.0 8.0
## 7564 6.0 5.0 6.0
## 7565 7.0 8.0 6.0
## 7569 7.0 5.0 7.0
## 7573 NA NA NA
## 7574 8.0 5.0 NA
## 7575 7.0 6.0 7.0
## 7580 3.0 2.0 6.0
## 7595 NA NA NA
## 7602 7.0 3.0 7.0
## 7606 4.0 5.0 4.0
## 7607 5.0 5.0 NA
## 7609 6.0 5.0 6.0
## 7610 7.0 5.0 5.0
## 7611 7.0 6.0 5.0
## 7612 5.0 5.0 5.0
## 7614 5.0 4.0 NA
## 7616 5.0 5.0 5.0
## 7617 5.0 5.0 NA
## 7618 7.0 5.0 5.0
## 7621 5.0 4.0 NA
## 7624 5.0 5.0 3.0
## 7627 5.0 4.0 6.0
## 7629 9.0 9.0 7.0
## 7655 10.0 5.0 10.0
## 7667 8.0 6.0 8.0
## 7670 NA NA NA
## 7675 7.0 7.0 7.0
## 7682 6.0 7.0 NA
## 7687 7.0 5.0 5.0
## 7689 8.0 5.0 NA
## 7692 6.0 6.0 NA
## 7693 5.0 5.0 NA
## 7694 NA NA NA
## 7695 7.0 7.0 NA
## 7696 8.0 8.0 NA
## 7697 7.0 6.0 7.0
## 7698 7.0 6.0 NA
## 7700 7.0 5.0 NA
## 7701 8.0 8.0 NA
## 7702 5.0 5.0 NA
## 7706 9.0 5.0 NA
## 7708 8.0 9.0 8.0
## 7712 7.0 7.0 8.0
## 7715 8.0 9.0 9.0
## 7719 8.0 8.0 7.0
## 7725 6.0 6.0 6.0
## 7735 8.0 7.0 6.0
## 7745 NA NA NA
## 7746 NA NA NA
## 7752 6.0 8.0 7.0
## 7754 NA NA NA
## 7755 7.0 7.0 6.0
## 7756 9.0 6.0 4.0
## 7757 7.0 5.0 6.0
## 7767 9.0 8.0 8.0
## 7768 NA NA NA
## 7770 8.0 8.0 8.0
## 7772 9.0 8.0 NA
## 7775 8.0 8.0 5.0
## 7776 8.0 8.0 6.0
## 7777 8.0 NA NA
## 7785 8.0 4.0 5.0
## 7788 5.0 8.0 6.0
## 7790 6.0 7.0 7.0
## 7791 6.0 7.0 5.0
## 7795 6.0 6.0 6.0
## 7796 5.0 5.0 5.0
## 7797 5.0 4.0 4.0
## 7799 6.0 6.0 5.0
## 7805 6.0 5.0 5.0
## 7808 6.0 5.0 4.0
## 7814 NA NA NA
## 7815 5.0 7.0 8.0
## 7825 9.0 8.0 7.0
## 7835 6.0 7.0 7.0
## 7836 6.0 6.0 5.0
## 7845 5.0 4.0 3.0
## 7847 NA NA NA
## 7850 NA NA NA
## 7855 6.0 8.0 6.0
## 7863 8.0 5.0 6.0
## 7872 6.0 7.0 4.0
## 7874 NA NA NA
## 7876 6.0 6.0 5.0
## 7879 4.0 6.0 7.0
## 7880 4.0 6.0 5.0
## 7885 5.0 4.0 3.0
## 7887 NA NA NA
## 7888 8.0 8.0 8.0
## 7892 7.0 7.0 8.0
## 7895 8.0 8.0 8.0
## 7896 NA NA NA
## 7908 7.0 6.0 6.0
## 7914 NA NA NA
## 7918 8.0 7.0 7.0
## 7919 7.0 7.0 7.0
## 7925 6.0 6.0 6.0
## 7928 NA NA NA
## 7929 NA NA NA
## 7931 NA NA NA
## 7932 NA NA NA
## 7933 NA NA NA
## 7938 7.0 7.0 7.0
## 7941 NA NA NA
## 7942 NA NA NA
## 7943 NA NA NA
## 7944 NA NA NA
## 7945 8.0 2.0 7.0
## 7952 5.0 6.0 8.0
## 7955 6.0 8.0 6.0
## 7957 NA NA NA
## 7958 6.0 3.0 6.0
## 7963 4.0 3.0 6.0
## 7965 7.0 4.0 5.0
## 7968 6.0 6.0 3.0
## 7972 7.0 5.0 5.0
## 7975 7.0 10.0 6.0
## 7995 7.0 8.0 2.0
## 7999 7.0 1.0 7.0
## 8008 9.0 9.0 8.0
## 8012 7.0 8.0 7.0
## 8014 0.0 0.0 0.0
## 8018 8.0 7.0 8.0
## 8019 8.0 8.0 7.0
## 8025 6.0 6.0 7.0
## 8032 7.0 6.0 4.0
## 8048 9.0 7.0 7.0
## 8052 8.0 7.0 7.0
## 8055 5.0 7.0 7.0
## 8056 NA NA NA
## 8067 9.0 8.0 9.0
## 8068 9.0 9.0 NA
## 8070 9.0 9.0 6.0
## 8072 5.0 5.0 5.0
## 8076 7.0 6.0 6.0
## 8081 8.0 8.0 NA
## 8082 5.0 5.0 6.0
## 8083 5.0 5.0 NA
## 8084 8.0 4.0 4.0
## 8085 5.0 4.0 NA
## shared_interests_partner like guess_prob_liked met decision
## 3 7.0 7.0 NA 1 1
## 8 6.0 6.0 7.0 NA 0
## 25 10.0 9.0 NA 0 0
## 26 9.0 8.0 NA 0 0
## 75 7.0 6.0 5.0 0 1
## 91 NA 6.0 3.0 0 0
## 92 NA 1.0 1.0 1 0
## 93 NA 7.0 6.0 0 1
## 94 NA 10.0 1.0 1 0
## 95 NA 6.0 1.0 0 0
## 96 NA 6.0 1.0 0 0
## 97 NA 7.0 1.0 0 1
## 98 NA 5.0 5.0 0 0
## 110 5.0 6.0 3.0 0 0
## 120 1.0 1.0 1.0 0 0
## 124 10.0 10.0 10.0 NA 1
## 127 10.0 10.0 10.0 NA 1
## 130 10.0 10.0 10.0 0 1
## 140 8.0 7.0 7.0 1 1
## 143 NA 6.0 1.0 0 1
## 148 7.0 9.0 7.0 0 1
## 150 NA 6.0 6.0 0 1
## 160 7.0 6.0 6.0 0 0
## 170 4.0 3.0 6.0 0 0
## 180 5.0 7.0 6.0 0 0
## 202 5.0 7.0 2.0 0 0
## 203 6.0 9.0 3.0 NA 1
## 211 6.0 6.0 1.0 0 0
## 218 2.0 3.0 3.0 0 0
## 224 5.0 5.0 4.0 0 0
## 225 6.0 7.0 4.0 0 1
## 226 4.0 5.0 3.0 0 0
## 227 7.0 6.0 6.0 0 1
## 234 7.0 7.0 6.0 0 0
## 235 6.0 6.5 5.0 0 1
## 243 7.0 7.0 5.0 0 1
## 246 NA NA NA NA 0
## 249 NA 6.0 3.0 0 0
## 250 NA 8.0 4.0 0 0
## 251 NA 7.0 5.0 0 0
## 252 NA 7.0 5.0 0 0
## 253 6.0 7.0 7.0 0 0
## 254 NA 8.0 6.0 0 0
## 255 8.0 8.0 7.0 0 1
## 257 NA 7.0 6.0 0 0
## 258 6.0 6.0 5.0 0 0
## 259 NA 7.0 7.0 0 0
## 260 NA 7.0 7.0 0 0
## 261 7.0 7.0 6.0 0 0
## 262 8.0 8.0 7.0 0 1
## 263 8.0 7.0 6.0 0 0
## 264 2.0 6.0 4.0 0 0
## 266 5.0 7.0 3.0 0 0
## 267 7.0 7.0 5.0 0 0
## 273 6.0 7.0 6.0 0 0
## 275 7.0 8.0 7.0 0 0
## 282 2.0 2.0 2.0 0 0
## 283 3.0 3.0 2.0 0 0
## 289 3.0 4.0 4.0 0 0
## 291 6.0 8.0 5.0 0 0
## 297 NA 3.0 3.0 0 0
## 298 NA 2.0 1.0 0 0
## 299 1.0 2.0 2.0 0 0
## 305 7.0 3.0 1.0 0 0
## 306 NA 2.0 3.0 0 0
## 307 4.0 4.0 4.0 0 1
## 313 NA 5.0 4.0 0 0
## 314 NA 4.0 4.0 0 0
## 315 NA 6.0 3.0 0 0
## 316 NA 5.0 4.0 NA 0
## 319 NA 7.0 4.0 0 1
## 320 NA 6.0 4.0 0 0
## 323 NA 7.0 5.0 0 1
## 326 NA 8.0 5.0 0 1
## 327 5.0 6.0 4.0 NA 0
## 330 5.0 8.0 5.0 0 0
## 331 7.0 8.0 6.0 0 0
## 337 7.0 8.0 7.0 0 0
## 338 7.0 9.0 7.0 1 1
## 339 7.0 8.0 4.0 0 1
## 346 2.0 NA NA 0 0
## 347 4.0 5.0 3.0 NA 0
## 353 8.0 8.0 8.0 0 1
## 354 5.0 7.0 8.0 0 1
## 355 8.0 9.0 9.0 1 1
## 361 6.0 5.0 5.0 0 0
## 362 7.0 7.0 5.0 0 0
## 363 NA 6.0 5.0 0 0
## 367 7.0 8.0 5.0 0 0
## 371 4.0 6.0 3.0 0 0
## 378 2.0 5.0 2.0 0 0
## 385 8.0 7.0 3.0 0 1
## 393 NA 5.0 6.0 0 0
## 394 NA 5.0 7.0 0 0
## 396 NA 5.0 4.0 0 0
## 399 NA 8.0 8.0 0 1
## 403 4.0 5.0 5.0 0 0
## 407 3.0 5.0 4.0 NA 0
## 408 5.0 5.0 3.0 NA 0
## 410 6.0 6.0 7.0 0 0
## 413 5.0 6.0 6.0 0 0
## 419 9.0 8.0 10.0 0 1
## 426 2.0 2.0 5.0 0 0
## 427 1.0 2.0 3.0 0 0
## 429 1.0 1.0 5.0 NA 0
## 435 3.0 5.0 3.0 0 0
## 442 3.0 3.0 5.0 0 0
## 443 4.0 4.0 5.0 0 1
## 448 6.0 5.0 5.0 0 0
## 451 4.0 5.0 4.0 0 1
## 456 5.0 4.0 4.0 0 1
## 458 1.0 1.0 1.0 0 0
## 459 7.0 8.0 1.0 0 0
## 465 3.0 8.0 4.0 0 1
## 467 7.0 7.0 6.0 1 1
## 474 NA 5.0 5.0 0 0
## 475 NA 6.5 6.0 0 1
## 479 NA 5.0 2.0 0 0
## 482 NA 6.0 3.0 0 0
## 483 NA 7.0 5.0 0 1
## 484 NA 1.0 1.0 1 0
## 485 NA 6.0 2.0 0 1
## 486 NA 7.0 5.0 0 1
## 487 NA 5.0 3.0 NA 0
## 488 NA 7.0 3.0 0 0
## 492 5.0 2.0 3.0 0 0
## 495 5.0 5.0 2.0 0 0
## 496 3.0 1.0 NA 0 0
## 499 4.0 5.0 1.0 0 0
## 501 4.0 5.0 2.0 0 0
## 507 NA 5.0 NA 0 0
## 508 NA 5.0 NA 0 0
## 509 NA 5.0 NA 0 0
## 510 NA 5.0 NA 0 0
## 511 NA 5.0 NA 0 1
## 512 NA 5.0 NA 0 1
## 513 NA 8.0 NA 0 1
## 514 NA 8.0 NA 0 0
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## 7735 7.0 8.0 7.0 0 1
## 7745 NA NA NA NA 0
## 7746 NA NA NA NA 0
## 7752 3.0 5.0 3.0 0 0
## 7754 NA NA NA NA 0
## 7755 4.0 6.0 4.0 0 0
## 7756 3.0 3.0 4.0 0 0
## 7757 6.0 2.0 2.0 0 0
## 7767 8.0 7.0 6.0 NA 1
## 7768 NA NA NA NA 0
## 7770 6.0 7.0 5.0 NA 1
## 7772 5.0 8.0 3.0 NA 0
## 7775 7.0 8.0 3.0 0 0
## 7776 6.0 8.0 3.0 0 1
## 7777 NA NA NA NA 0
## 7785 2.0 4.0 4.0 0 0
## 7788 5.0 6.0 5.0 0 1
## 7790 6.0 5.0 5.0 0 1
## 7791 5.0 6.0 7.0 0 0
## 7795 6.0 6.0 8.0 0 0
## 7796 5.0 6.0 10.0 0 1
## 7797 5.0 4.0 4.0 0 0
## 7799 5.0 5.0 8.0 0 0
## 7805 4.0 6.0 2.0 1 0
## 7808 7.0 7.0 5.0 0 1
## 7814 NA NA NA NA 0
## 7815 3.0 6.0 4.0 0 0
## 7825 5.0 8.0 4.0 0 1
## 7835 4.0 7.0 4.0 0 1
## 7836 2.0 5.0 3.0 0 0
## 7845 2.0 6.0 3.0 0 0
## 7847 NA NA NA NA 0
## 7850 NA NA NA NA 0
## 7855 4.0 6.0 4.0 0 1
## 7863 6.0 5.0 6.0 0 0
## 7872 2.0 8.0 4.0 0 1
## 7874 NA NA NA NA 0
## 7876 7.0 7.0 2.0 0 0
## 7879 2.0 6.0 7.0 0 1
## 7880 6.0 2.0 8.0 0 0
## 7885 5.0 7.0 7.0 0 0
## 7887 NA NA NA NA 0
## 7888 5.0 7.0 5.0 0 0
## 7892 3.0 6.0 4.0 0 0
## 7895 5.0 4.0 5.0 0 0
## 7896 NA NA NA NA 0
## 7908 5.0 6.0 5.0 0 0
## 7914 NA NA NA NA 0
## 7918 6.0 6.0 5.0 0 1
## 7919 7.0 7.0 7.0 0 1
## 7925 6.0 6.0 5.0 0 0
## 7928 NA NA NA NA 0
## 7929 NA NA NA NA 0
## 7931 NA NA NA NA 0
## 7932 NA NA NA NA 0
## 7933 NA NA NA NA 0
## 7938 5.0 7.0 NA 0 1
## 7941 NA NA NA NA 0
## 7942 NA NA NA NA 0
## 7943 NA NA NA NA 0
## 7944 NA NA NA NA 0
## 7945 2.0 5.0 5.0 0 0
## 7952 6.0 5.0 6.0 0 0
## 7955 7.0 7.0 9.0 0 1
## 7957 NA NA NA NA 0
## 7958 6.0 5.0 6.0 0 0
## 7963 6.0 5.0 7.0 0 0
## 7965 2.0 4.0 5.0 0 0
## 7968 2.0 6.0 3.0 0 0
## 7972 1.0 5.0 5.0 0 0
## 7975 6.0 8.0 6.0 0 1
## 7995 5.0 8.0 7.0 0 1
## 7999 1.0 1.0 1.0 0 0
## 8008 7.0 9.0 5.0 0 1
## 8012 5.0 6.0 5.0 0 1
## 8014 0.0 0.0 0.0 0 0
## 8018 7.0 6.0 6.0 0 1
## 8019 5.0 6.0 4.0 0 1
## 8025 6.0 6.0 6.0 0 0
## 8032 4.0 5.0 5.0 0 0
## 8048 8.0 8.0 5.0 0 1
## 8052 5.0 8.0 3.0 0 1
## 8055 5.0 7.0 3.0 0 1
## 8056 NA NA NA NA 0
## 8067 7.0 8.0 5.0 0 1
## 8068 7.0 9.0 8.0 0 1
## 8070 8.0 9.0 6.0 NA 1
## 8072 4.0 8.0 7.0 0 1
## 8076 NA 6.0 4.0 0 0
## 8081 NA 7.0 6.0 0 1
## 8082 NA 4.0 4.0 0 0
## 8083 NA 2.0 5.0 0 0
## 8084 NA 4.0 4.0 0 0
## 8085 5.0 5.0 5.0 0 0columns_of_interest <- c("attractive_o", "sincere_o", "intelligence_o", "funny_o", "ambitious_o", "shared_interests_o",
"attractive_partner", "sincere_partner", "intelligence_partner", "funny_partner",
"ambition_partner", "shared_interests_partner", "like", "guess_prob_liked", "met")
threshold <- length(columns_of_interest) / 2
partially_filtered_dates <- dates[rowSums(is.na(dates[, columns_of_interest])) <= threshold, ]
partially_filtered_dates## date_id participant_id partner_id attractive_o sincere_o intelligence_o
## 1 100000 10000 10010 6.0 8.0 8.0
## 2 100001 10000 10011 7.0 8.0 10.0
## 3 100002 10000 10012 10.0 10.0 10.0
## 4 100003 10000 10013 7.0 8.0 9.0
## 5 100004 10000 10014 8.0 7.0 9.0
## 6 100005 10000 10015 7.0 7.0 8.0
## 7 100006 10000 10016 3.0 6.0 7.0
## 8 100007 10000 10017 6.0 7.0 5.0
## 9 100008 10000 10018 7.0 7.0 8.0
## 10 100009 10000 10019 6.0 6.0 6.0
## 11 100010 10001 10010 8.0 7.0 6.0
## 12 100011 10001 10011 7.0 6.0 10.0
## 13 100012 10001 10012 10.0 10.0 10.0
## 14 100013 10001 10013 9.0 9.0 9.0
## 15 100014 10001 10014 10.0 10.0 10.0
## 16 100015 10001 10015 7.0 8.0 7.0
## 17 100016 10001 10016 5.0 3.0 4.0
## 18 100017 10001 10017 7.0 7.0 7.0
## 19 100018 10001 10018 8.0 6.0 9.0
## 20 100019 10001 10019 6.0 5.0 7.0
## 21 100020 10002 10010 7.0 8.0 6.0
## 22 100021 10002 10011 6.0 7.0 10.0
## 23 100022 10002 10012 10.0 10.0 10.0
## 24 100023 10002 10013 7.0 9.0 8.0
## 25 100024 10002 10014 6.0 10.0 8.0
## 26 100025 10002 10015 7.0 6.0 6.0
## 27 100026 10002 10016 6.0 3.0 5.0
## 28 100027 10002 10017 4.0 5.0 6.0
## 29 100028 10002 10018 7.0 7.0 6.0
## 30 100029 10002 10019 5.0 6.0 8.0
## 31 100030 10003 10010 6.0 7.0 8.0
## 32 100031 10003 10011 6.0 5.0 10.0
## 33 100032 10003 10012 10.0 10.0 10.0
## 34 100033 10003 10013 7.0 7.0 7.0
## 35 100034 10003 10014 8.0 8.0 9.0
## 36 100035 10003 10015 6.0 6.0 7.0
## 37 100036 10003 10016 7.0 6.0 3.0
## 38 100037 10003 10017 6.0 7.0 8.0
## 39 100038 10003 10018 7.0 7.0 7.0
## 40 100039 10003 10019 7.0 8.0 8.0
## 41 100040 10004 10010 6.0 8.0 8.0
## 42 100041 10004 10011 6.0 7.0 10.0
## 43 100042 10004 10012 10.0 10.0 10.0
## 44 100043 10004 10013 6.0 8.0 6.0
## 45 100044 10004 10014 5.0 9.0 9.0
## 46 100045 10004 10015 5.0 8.0 7.0
## 47 100046 10004 10016 2.0 3.0 4.0
## 48 100047 10004 10017 5.0 5.0 6.0
## 49 100048 10004 10018 5.0 9.0 9.0
## 50 100049 10004 10019 3.0 10.0 7.0
## 51 100050 10005 10010 7.0 9.0 8.0
## 52 100051 10005 10011 6.0 8.0 10.0
## 53 100052 10005 10012 10.0 10.0 10.0
## 54 100053 10005 10013 6.0 8.0 8.0
## 55 100054 10005 10014 9.0 7.0 9.0
## 56 100055 10005 10015 6.0 6.0 8.0
## 57 100056 10005 10016 5.0 6.0 8.0
## 58 100057 10005 10017 6.0 7.0 8.0
## 59 100058 10005 10018 6.0 7.0 7.0
## 60 100059 10005 10019 7.0 10.0 10.0
## 61 100060 10006 10010 7.0 8.0 8.0
## 62 100061 10006 10011 7.0 9.0 10.0
## 63 100062 10006 10012 10.0 10.0 10.0
## 64 100063 10006 10013 8.0 8.0 9.0
## 65 100064 10006 10014 8.0 7.0 9.0
## 66 100065 10006 10015 8.0 8.0 8.0
## 67 100066 10006 10016 7.0 6.0 5.0
## 68 100067 10006 10017 8.0 7.0 6.0
## 69 100068 10006 10018 9.0 7.0 9.0
## 70 100069 10006 10019 7.0 6.0 8.0
## 71 100070 10007 10010 8.0 7.0 6.0
## 72 100071 10007 10011 8.0 8.0 10.0
## 73 100072 10007 10012 10.0 10.0 10.0
## 74 100073 10007 10013 8.0 8.0 8.0
## 75 100074 10007 10014 9.0 9.0 8.0
## 76 100075 10007 10015 8.0 7.0 7.0
## 77 100076 10007 10016 6.0 4.0 7.0
## 78 100077 10007 10017 9.0 8.0 8.0
## 79 100078 10007 10018 8.0 7.0 8.0
## 80 100079 10007 10019 8.0 10.0 9.0
## 81 100080 10008 10010 6.0 8.0 7.0
## 82 100081 10008 10011 8.0 8.0 10.0
## 83 100082 10008 10012 10.0 10.0 10.0
## 84 100083 10008 10013 7.0 8.0 7.0
## 85 100084 10008 10014 7.0 9.0 8.0
## 86 100085 10008 10015 6.0 6.0 7.0
## 87 100086 10008 10016 8.0 6.0 7.0
## 88 100087 10008 10017 7.0 7.0 7.0
## 89 100088 10008 10018 6.0 6.0 6.0
## 90 100089 10008 10019 5.0 6.0 6.0
## 91 100090 10009 10010 7.0 8.0 7.0
## 92 100091 10009 10011 1.0 1.0 1.0
## 93 100092 10009 10012 10.0 10.0 10.0
## 94 100093 10009 10013 7.0 8.0 7.0
## 95 100094 10009 10014 6.0 9.0 9.0
## 96 100095 10009 10015 NA 8.0 7.0
## 97 100096 10009 10016 7.0 7.0 4.0
## 98 100097 10009 10017 6.0 6.0 6.0
## 99 100098 10009 10018 7.0 7.0 7.0
## 100 100099 10009 10019 6.0 6.0 6.0
## 101 100100 10010 10000 6.0 9.0 7.0
## 102 100101 10010 10001 5.0 7.0 8.0
## 103 100102 10010 10002 7.0 9.0 10.0
## 104 100103 10010 10003 4.0 10.0 8.0
## 105 100104 10010 10004 5.0 8.0 8.0
## 106 100105 10010 10005 6.0 9.0 7.0
## 107 100106 10010 10006 4.0 8.0 8.0
## 108 100107 10010 10007 5.0 8.0 7.0
## 109 100108 10010 10008 8.0 10.0 9.0
## 110 100109 10010 10009 6.0 10.0 10.0
## 111 100110 10011 10000 7.0 8.0 7.0
## 112 100111 10011 10001 8.0 5.0 6.0
## 113 100112 10011 10002 9.0 7.0 9.0
## 114 100113 10011 10003 8.0 7.0 8.0
## 115 100114 10011 10004 8.0 6.0 6.0
## 116 100115 10011 10005 6.0 9.0 9.0
## 117 100116 10011 10006 6.0 6.0 8.0
## 118 100117 10011 10007 8.0 7.0 7.0
## 119 100118 10011 10008 10.0 10.0 10.0
## 120 100119 10011 10009 1.0 1.0 1.0
## 121 100120 10012 10000 5.0 8.0 9.0
## 122 100121 10012 10001 5.0 8.0 9.0
## 123 100122 10012 10002 7.0 9.0 9.0
## 124 100123 10012 10003 4.0 7.0 8.0
## 125 100124 10012 10004 4.0 6.0 8.0
## 126 100125 10012 10005 4.0 7.0 9.0
## 127 100126 10012 10006 3.0 6.0 8.0
## 128 100127 10012 10007 5.0 8.0 8.0
## 129 100128 10012 10008 5.0 10.0 10.0
## 130 100129 10012 10009 6.0 10.0 10.0
## 131 100130 10013 10000 7.0 6.0 8.0
## 132 100131 10013 10001 7.0 9.0 7.0
## 133 100132 10013 10002 9.0 7.0 9.0
## 134 100133 10013 10003 8.0 10.0 7.0
## 135 100134 10013 10004 8.0 5.0 5.0
## 136 100135 10013 10005 8.0 6.0 7.0
## 137 100136 10013 10006 7.0 8.0 8.0
## 138 100137 10013 10007 8.0 6.0 5.0
## 139 100138 10013 10008 10.0 9.0 8.0
## 140 100139 10013 10009 10.0 10.0 10.0
## 141 100140 10014 10000 5.0 6.0 7.0
## 142 100141 10014 10001 6.0 8.0 7.0
## 143 100142 10014 10002 9.0 10.0 10.0
## 144 100143 10014 10003 6.0 9.0 8.0
## 145 100144 10014 10004 6.0 7.0 7.0
## 146 100145 10014 10005 5.0 4.0 8.0
## 147 100146 10014 10006 4.0 7.0 7.0
## 148 100147 10014 10007 6.0 8.0 8.0
## 149 100148 10014 10008 5.0 10.0 8.0
## 150 100149 10014 10009 5.0 7.0 7.0
## 151 100150 10015 10000 4.0 9.0 7.0
## 152 100151 10015 10001 8.0 7.0 8.0
## 153 100152 10015 10002 8.0 10.0 10.0
## 154 100153 10015 10003 5.0 10.0 8.0
## 155 100154 10015 10004 5.0 6.0 7.0
## 156 100155 10015 10005 3.0 7.0 6.0
## 157 100156 10015 10006 4.0 8.0 7.0
## 158 100157 10015 10007 6.0 8.0 8.0
## 159 100158 10015 10008 8.0 9.0 9.0
## 160 100159 10015 10009 5.0 10.0 6.0
## 161 100160 10016 10000 7.0 6.0 7.0
## 162 100161 10016 10001 7.0 5.0 9.0
## 163 100162 10016 10002 8.0 9.0 10.0
## 164 100163 10016 10003 7.0 10.0 10.0
## 165 100164 10016 10004 6.0 6.0 8.0
## 166 100165 10016 10005 8.0 8.0 8.0
## 167 100166 10016 10006 7.0 7.0 8.0
## 168 100167 10016 10007 6.0 7.0 8.0
## 169 100168 10016 10008 6.0 8.0 9.0
## 170 100169 10016 10009 5.0 10.0 8.0
## 171 100170 10017 10000 4.0 9.0 7.0
## 172 100171 10017 10001 5.0 8.0 7.0
## 173 100172 10017 10002 7.0 9.0 9.0
## 174 100173 10017 10003 5.0 9.0 9.0
## 175 100174 10017 10004 5.0 5.0 7.0
## 176 100175 10017 10005 4.0 8.0 9.0
## 177 100176 10017 10006 5.0 7.0 8.0
## 178 100177 10017 10007 5.0 6.0 7.0
## 179 100178 10017 10008 6.0 7.0 8.0
## 180 100179 10017 10009 5.0 6.0 6.0
## 181 100180 10018 10000 7.0 6.0 8.0
## 182 100181 10018 10001 7.0 6.0 8.0
## 183 100182 10018 10002 9.0 9.0 9.0
## 184 100183 10018 10003 9.0 8.0 10.0
## 185 100184 10018 10004 7.0 5.0 7.0
## 186 100185 10018 10005 7.0 6.0 8.0
## 187 100186 10018 10006 6.0 6.0 8.0
## 188 100187 10018 10007 8.0 8.0 5.0
## 189 100188 10018 10008 8.0 5.0 5.0
## 190 100189 10018 10009 9.0 9.0 9.0
## 191 100190 10019 10000 5.0 6.0 6.0
## 192 100191 10019 10001 6.0 7.0 8.0
## 193 100192 10019 10002 8.0 7.0 9.0
## 194 100193 10019 10003 8.0 9.0 10.0
## 195 100194 10019 10004 9.0 6.0 7.0
## 196 100195 10019 10005 8.0 9.0 9.0
## 197 100196 10019 10006 8.0 6.0 7.0
## 198 100197 10019 10007 7.0 6.0 8.0
## 199 100198 10019 10008 7.0 8.0 7.0
## 200 100199 10019 10009 9.0 9.0 8.0
## 201 100200 10020 10039 3.0 10.0 10.0
## 202 100201 10020 10040 5.0 5.0 5.0
## 203 100202 10020 10041 3.0 10.0 9.0
## 204 100203 10020 10042 4.0 5.0 6.0
## 205 100204 10020 10043 6.0 7.0 8.0
## 206 100205 10020 10044 7.0 8.0 8.0
## 207 100206 10020 10045 6.0 7.0 10.0
## 208 100207 10020 10046 9.0 5.0 6.0
## 209 100208 10020 10047 3.0 8.0 5.0
## 210 100209 10020 10048 6.0 7.0 8.0
## 211 100210 10020 10049 5.0 8.0 8.0
## 212 100211 10020 10050 2.0 8.0 8.0
## 213 100212 10020 10051 6.0 6.0 8.0
## 214 100213 10020 10052 6.0 8.0 8.0
## 215 100214 10020 10053 5.0 7.0 7.0
## 216 100215 10020 10054 5.0 8.0 10.0
## 217 100216 10021 10039 4.0 10.0 8.0
## 218 100217 10021 10040 5.0 5.0 5.0
## 219 100218 10021 10041 3.0 6.0 8.0
## 220 100219 10021 10042 4.0 5.0 5.0
## 221 100220 10021 10043 6.0 7.0 6.0
## 222 100221 10021 10044 7.0 7.0 7.0
## 223 100222 10021 10045 5.0 8.0 5.0
## 224 100223 10021 10046 NA 5.0 6.0
## 225 100224 10021 10047 4.0 6.0 6.0
## 226 100225 10021 10048 7.0 8.0 7.0
## 227 100226 10021 10049 6.0 8.0 7.0
## 228 100227 10021 10050 2.0 2.0 3.0
## 229 100228 10021 10051 4.0 6.0 7.0
## 230 100229 10021 10052 6.0 8.0 8.0
## 231 100230 10021 10053 4.0 7.0 7.0
## 232 100231 10021 10054 5.0 8.0 9.0
## 233 100232 10022 10039 5.0 4.0 3.0
## 234 100233 10022 10040 8.0 8.0 8.0
## 235 100234 10022 10041 6.0 10.0 9.0
## 236 100235 10022 10042 4.0 6.0 5.0
## 237 100236 10022 10043 8.0 8.0 8.0
## 238 100237 10022 10044 7.0 8.0 7.0
## 239 100238 10022 10045 6.0 8.0 9.0
## 240 100239 10022 10046 5.0 6.0 6.0
## 241 100240 10022 10047 4.0 6.0 6.0
## 242 100241 10022 10048 7.0 8.0 8.0
## 243 100242 10022 10049 5.0 NA 7.0
## 244 100243 10022 10050 4.0 6.0 4.0
## 245 100244 10022 10051 7.0 7.0 6.0
## 247 100246 10022 10053 6.0 7.0 7.0
## 248 100247 10022 10054 6.0 9.0 10.0
## 249 100248 10023 10039 5.0 10.0 9.0
## 250 100249 10023 10040 5.0 5.0 5.0
## 251 100250 10023 10041 7.0 6.0 7.0
## 252 100251 10023 10042 5.0 5.0 6.0
## 253 100252 10023 10043 5.0 7.0 8.0
## 254 100253 10023 10044 6.0 6.0 6.0
## 255 100254 10023 10045 7.0 8.0 8.0
## 256 100255 10023 10046 6.0 6.0 6.0
## 257 100256 10023 10047 6.0 8.0 8.0
## 258 100257 10023 10048 7.0 7.0 6.0
## 259 100258 10023 10049 7.0 NA NA
## 260 100259 10023 10050 7.0 5.0 7.0
## 261 100260 10023 10051 7.0 7.0 9.0
## 262 100261 10023 10052 8.0 8.0 8.0
## 263 100262 10023 10053 8.0 7.0 8.0
## 264 100263 10023 10054 8.0 9.0 9.0
## 265 100264 10024 10039 8.0 10.0 9.0
## 266 100265 10024 10040 5.0 5.0 5.0
## 267 100266 10024 10041 8.0 9.0 7.0
## 268 100267 10024 10042 8.0 5.0 6.0
## 269 100268 10024 10043 7.0 6.0 6.0
## 270 100269 10024 10044 7.0 6.0 6.0
## 271 100270 10024 10045 10.0 8.0 7.0
## 272 100271 10024 10046 6.0 7.0 5.0
## 273 100272 10024 10047 9.0 8.0 7.0
## 274 100273 10024 10048 9.0 8.0 7.0
## 275 100274 10024 10049 7.0 NA NA
## 276 100275 10024 10050 6.0 4.0 7.0
## 277 100276 10024 10051 8.0 7.0 7.0
## 278 100277 10024 10052 9.0 8.0 8.0
## 279 100278 10024 10053 8.0 9.0 9.0
## 280 100279 10024 10054 8.0 9.0 8.0
## 281 100280 10025 10039 8.0 9.0 9.0
## 282 100281 10025 10040 5.0 5.0 5.0
## 283 100282 10025 10041 7.0 8.0 8.0
## 284 100283 10025 10042 6.0 5.0 7.0
## 285 100284 10025 10043 8.0 7.0 7.0
## 286 100285 10025 10044 7.0 6.0 8.0
## 287 100286 10025 10045 10.0 9.0 9.0
## 288 100287 10025 10046 5.0 6.0 6.0
## 289 100288 10025 10047 9.0 8.0 7.0
## 290 100289 10025 10048 9.0 8.0 7.0
## 291 100290 10025 10049 8.0 NA NA
## 292 100291 10025 10050 7.0 3.0 5.0
## 293 100292 10025 10051 8.0 7.0 7.0
## 294 100293 10025 10052 9.0 9.0 9.0
## 295 100294 10025 10053 8.0 8.0 7.0
## 296 100295 10025 10054 8.0 9.0 8.0
## 297 100296 10026 10039 9.0 8.0 6.0
## 298 100297 10026 10040 8.0 8.0 8.0
## 299 100298 10026 10041 8.0 9.0 8.0
## 300 100299 10026 10042 7.0 6.0 5.0
## 301 100300 10026 10043 9.0 7.0 7.0
## 302 100301 10026 10044 8.0 8.0 7.0
## 303 100302 10026 10045 9.0 8.0 8.0
## 304 100303 10026 10046 7.0 5.0 6.0
## 305 100304 10026 10047 8.0 8.0 5.0
## 306 100305 10026 10048 8.0 8.0 7.0
## 307 100306 10026 10049 8.0 NA NA
## 308 100307 10026 10050 8.0 4.0 4.0
## 309 100308 10026 10051 8.0 7.0 7.0
## 310 100309 10026 10052 8.0 8.0 8.0
## 311 100310 10026 10053 9.0 9.0 8.0
## 312 100311 10026 10054 9.0 8.0 9.0
## 313 100328 10028 10039 5.0 6.0 3.0
## 314 100329 10028 10040 8.0 8.0 8.0
## 315 100330 10028 10041 3.0 8.0 7.0
## 316 100331 10028 10042 4.0 4.0 5.0
## 317 100332 10028 10043 7.0 6.0 7.0
## 318 100333 10028 10044 8.0 8.0 6.0
## 319 100334 10028 10045 6.0 7.0 6.0
## 320 100335 10028 10046 5.0 6.0 5.0
## 321 100336 10028 10047 6.0 8.0 9.0
## 322 100337 10028 10048 7.0 8.0 8.0
## 323 100338 10028 10049 6.0 NA NA
## 324 100339 10028 10050 5.0 8.0 5.0
## 325 100340 10028 10051 5.0 7.0 6.0
## 326 100341 10028 10052 6.0 8.0 8.0
## 327 100342 10028 10053 5.0 8.0 7.0
## 328 100343 10028 10054 6.0 8.0 8.0
## 329 100344 10029 10039 6.0 6.0 8.0
## 330 100345 10029 10040 10.0 10.0 10.0
## 331 100346 10029 10041 4.0 7.0 7.0
## 332 100347 10029 10042 3.0 4.0 7.0
## 333 100348 10029 10043 7.0 7.0 7.0
## 334 100349 10029 10044 6.0 6.0 6.0
## 335 100350 10029 10045 6.0 8.0 8.0
## 336 100351 10029 10046 9.0 5.0 6.0
## 337 100352 10029 10047 5.0 6.0 5.0
## 338 100353 10029 10048 7.0 7.0 7.0
## 339 100354 10029 10049 6.0 NA NA
## 340 100355 10029 10050 3.0 5.0 6.0
## 341 100356 10029 10051 6.0 7.0 7.0
## 342 100357 10029 10052 6.0 8.0 8.0
## 343 100358 10029 10053 6.0 8.0 8.0
## 344 100359 10029 10054 7.0 9.0 10.0
## 345 100360 10030 10039 7.0 5.0 4.0
## 346 100361 10030 10040 8.0 8.0 8.0
## 347 100362 10030 10041 8.0 7.0 8.0
## 348 100363 10030 10042 6.0 6.0 6.0
## 349 100364 10030 10043 9.0 7.0 7.0
## 350 100365 10030 10044 9.0 8.0 8.0
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## 1586 101657 10111 10121 9.0 7.0 7.0
## 1587 101658 10111 10122 6.0 7.0 7.0
## 1588 101659 10111 10123 9.0 8.0 9.0
## 1589 101660 10111 10124 7.0 8.0 7.0
## 1590 101661 10111 10125 10.0 7.0 9.0
## 1591 101662 10111 10126 10.0 9.0 8.0
## 1592 101665 10111 10129 5.0 5.0 5.0
## 1593 101666 10112 10120 10.0 10.0 10.0
## 1594 101667 10112 10121 6.0 5.0 5.0
## 1595 101668 10112 10122 7.0 6.0 7.0
## 1596 101669 10112 10123 8.0 8.0 7.0
## 1597 101670 10112 10124 6.0 8.0 9.0
## 1598 101671 10112 10125 5.0 5.0 5.0
## 1599 101672 10112 10126 7.0 9.0 9.0
## 1600 101675 10112 10129 5.0 5.0 5.0
## 1601 101676 10113 10120 6.0 10.0 7.0
## 1602 101677 10113 10121 4.0 6.0 6.0
## 1603 101678 10113 10122 5.0 6.0 8.0
## 1604 101679 10113 10123 5.0 8.0 8.0
## 1605 101680 10113 10124 6.0 7.0 7.0
## 1606 101681 10113 10125 5.0 7.0 7.0
## 1607 101682 10113 10126 7.0 9.0 9.0
## 1608 101685 10113 10129 5.0 5.0 5.0
## 1609 101686 10114 10120 10.0 10.0 10.0
## 1610 101687 10114 10121 4.0 5.0 4.0
## 1611 101688 10114 10122 6.0 2.0 4.0
## 1612 101689 10114 10123 10.0 10.0 9.0
## 1613 101690 10114 10124 5.0 7.0 6.0
## 1614 101691 10114 10125 7.0 3.0 7.0
## 1615 101692 10114 10126 9.0 7.0 6.0
## 1617 101696 10115 10120 10.0 10.0 10.0
## 1618 101697 10115 10121 6.0 5.0 5.0
## 1619 101698 10115 10122 5.0 4.0 3.0
## 1620 101699 10115 10123 10.0 10.0 10.0
## 1621 101700 10115 10124 7.0 6.0 8.0
## 1622 101701 10115 10125 7.0 7.0 7.0
## 1623 101702 10115 10126 7.0 7.0 7.0
## 1624 101705 10115 10129 5.0 5.0 5.0
## 1625 101706 10116 10120 10.0 10.0 10.0
## 1626 101707 10116 10121 6.0 6.0 5.0
## 1627 101708 10116 10122 8.0 7.0 6.0
## 1628 101709 10116 10123 10.0 5.0 6.0
## 1629 101710 10116 10124 7.0 7.0 7.0
## 1630 101711 10116 10125 7.0 7.0 8.0
## 1631 101712 10116 10126 8.0 8.0 9.0
## 1633 101716 10117 10120 9.0 9.0 8.0
## 1634 101717 10117 10121 6.0 4.0 5.0
## 1635 101718 10117 10122 9.0 8.0 4.0
## 1636 101719 10117 10123 8.0 8.0 6.0
## 1637 101720 10117 10124 6.0 9.0 8.0
## 1638 101721 10117 10125 9.0 8.0 8.0
## 1639 101722 10117 10126 9.0 8.0 9.0
## 1640 101725 10117 10129 6.0 6.0 6.0
## 1641 101726 10118 10120 6.0 7.0 7.0
## 1642 101727 10118 10121 9.0 6.0 8.0
## 1643 101728 10118 10122 7.0 5.0 6.0
## 1644 101729 10118 10123 9.0 8.0 7.0
## 1645 101730 10118 10124 7.0 7.0 7.0
## 1646 101731 10118 10125 8.0 5.0 7.0
## 1647 101732 10118 10126 8.0 8.0 10.0
## 1648 101735 10118 10129 6.0 6.0 7.0
## 1649 101736 10119 10120 6.0 9.0 8.0
## 1650 101737 10119 10121 5.0 4.0 5.0
## 1651 101738 10119 10122 2.0 7.0 9.0
## 1652 101739 10119 10123 4.0 6.0 6.0
## 1653 101740 10119 10124 4.0 8.0 7.0
## 1654 101741 10119 10125 5.0 8.0 7.0
## 1655 101742 10119 10126 6.0 9.0 7.0
## 1656 101745 10119 10129 7.0 5.0 10.0
## 1657 101746 10120 10111 7.0 7.0 7.0
## 1658 101747 10120 10112 8.0 10.0 9.0
## 1659 101748 10120 10113 7.0 6.0 6.0
## 1660 101749 10120 10114 10.0 10.0 10.0
## 1661 101750 10120 10115 10.0 10.0 10.0
## 1662 101751 10120 10116 10.0 10.0 10.0
## 1663 101752 10120 10117 3.0 8.0 5.0
## 1664 101753 10120 10118 7.0 8.0 7.0
## 1665 101754 10120 10119 8.0 10.0 10.0
## 1666 101756 10121 10111 4.0 8.0 7.0
## 1667 101757 10121 10112 5.0 9.0 6.0
## 1668 101758 10121 10113 8.0 7.0 6.0
## 1669 101759 10121 10114 6.0 10.0 10.0
## 1670 101760 10121 10115 7.0 10.0 8.0
## 1671 101761 10121 10116 7.0 10.0 8.0
## 1672 101762 10121 10117 NA 6.0 7.0
## 1673 101763 10121 10118 5.0 8.0 6.0
## 1674 101764 10121 10119 9.0 10.0 7.0
## 1675 101766 10122 10111 5.0 1.0 6.0
## 1676 101767 10122 10112 5.0 9.0 8.0
## 1677 101768 10122 10113 4.0 3.0 4.0
## 1678 101769 10122 10114 2.0 10.0 10.0
## 1679 101770 10122 10115 4.0 4.0 8.0
## 1680 101771 10122 10116 4.0 8.0 7.0
## 1681 101772 10122 10117 4.0 9.0 8.0
## 1682 101773 10122 10118 2.0 1.0 4.0
## 1683 101774 10122 10119 4.0 6.0 7.0
## 1684 101776 10123 10111 7.0 7.0 8.0
## 1685 101777 10123 10112 10.0 9.0 10.0
## 1686 101778 10123 10113 7.0 5.0 5.0
## 1687 101779 10123 10114 10.0 10.0 10.0
## 1688 101780 10123 10115 10.0 8.0 8.0
## 1689 101781 10123 10116 10.0 10.0 10.0
## 1690 101782 10123 10117 6.0 8.0 8.0
## 1691 101783 10123 10118 6.0 8.0 8.0
## 1692 101784 10123 10119 6.0 10.0 10.0
## 1693 101786 10124 10111 5.0 7.0 9.0
## 1694 101787 10124 10112 6.0 9.0 8.0
## 1695 101788 10124 10113 5.0 6.0 5.0
## 1696 101789 10124 10114 6.0 10.0 10.0
## 1697 101790 10124 10115 6.0 9.0 10.0
## 1698 101791 10124 10116 6.0 10.0 9.0
## 1699 101792 10124 10117 5.0 4.0 8.0
## 1700 101793 10124 10118 8.0 10.0 8.0
## 1701 101794 10124 10119 8.0 7.0 10.0
## 1702 101796 10125 10111 10.0 6.0 7.0
## 1703 101797 10125 10112 9.0 9.0 8.0
## 1704 101798 10125 10113 2.0 3.0 2.0
## 1705 101799 10125 10114 8.0 5.0 9.0
## 1706 101800 10125 10115 8.0 6.0 6.0
## 1707 101801 10125 10116 8.0 6.0 7.0
## 1708 101802 10125 10117 8.0 10.0 9.0
## 1709 101803 10125 10118 9.0 4.0 5.0
## 1710 101804 10125 10119 4.0 4.0 2.0
## 1711 101806 10126 10111 9.0 10.0 8.0
## 1712 101807 10126 10112 6.0 10.0 9.0
## 1713 101808 10126 10113 8.0 6.0 7.0
## 1714 101809 10126 10114 9.0 10.0 9.0
## 1715 101810 10126 10115 7.0 8.0 8.0
## 1716 101811 10126 10116 7.0 8.0 6.0
## 1717 101812 10126 10117 5.0 7.0 NA
## 1718 101813 10126 10118 8.0 7.0 8.0
## 1719 101814 10126 10119 9.0 10.0 10.0
## 1720 101836 10129 10111 2.0 7.0 6.0
## 1721 101837 10129 10112 4.0 9.0 8.0
## 1722 101838 10129 10113 5.0 6.0 4.0
## 1724 101840 10129 10115 4.0 8.0 7.0
## 1726 101842 10129 10117 2.0 7.0 6.0
## 1727 101843 10129 10118 7.0 8.0 6.0
## 1728 101844 10129 10119 7.0 10.0 10.0
## 1729 101846 10130 10135 8.0 8.0 9.0
## 1730 101847 10130 10136 8.0 7.0 4.0
## 1731 101848 10130 10137 8.0 NA 8.0
## 1732 101849 10130 10138 7.0 7.0 7.0
## 1733 101850 10130 10139 8.0 7.0 7.0
## 1734 101851 10131 10135 6.0 10.0 10.0
## 1735 101852 10131 10136 3.0 8.0 8.0
## 1736 101853 10131 10137 6.0 8.0 10.0
## 1737 101854 10131 10138 5.0 9.0 8.0
## 1738 101855 10131 10139 6.0 10.0 9.0
## 1739 101856 10132 10135 9.0 9.0 9.0
## 1740 101857 10132 10136 3.0 8.0 8.0
## 1741 101858 10132 10137 7.0 9.0 9.0
## 1742 101859 10132 10138 6.5 8.0 7.5
## 1743 101860 10132 10139 7.0 8.0 8.0
## 1744 101861 10133 10135 7.0 8.0 8.0
## 1745 101862 10133 10136 4.0 6.0 6.0
## 1746 101863 10133 10137 6.0 NA NA
## 1747 101864 10133 10138 6.5 8.0 8.0
## 1748 101865 10133 10139 8.0 9.0 8.0
## 1749 101871 10135 10130 7.0 9.0 7.0
## 1750 101872 10135 10131 7.0 7.0 7.0
## 1751 101873 10135 10132 8.0 9.0 7.0
## 1752 101874 10135 10133 8.0 5.0 NA
## 1753 101876 10136 10130 8.0 8.0 8.0
## 1754 101877 10136 10131 7.0 6.0 8.0
## 1755 101878 10136 10132 7.0 8.0 9.0
## 1756 101879 10136 10133 8.0 8.0 8.0
## 1757 101881 10137 10130 5.0 7.0 9.0
## 1758 101882 10137 10131 6.0 5.0 5.0
## 1759 101883 10137 10132 7.0 7.0 8.0
## 1760 101884 10137 10133 7.0 8.0 NA
## 1761 101886 10138 10130 8.0 9.0 9.0
## 1762 101887 10138 10131 8.0 7.0 7.0
## 1763 101888 10138 10132 8.0 9.0 9.0
## 1764 101889 10138 10133 8.0 8.0 8.0
## 1765 101891 10139 10130 5.0 8.0 8.0
## 1766 101892 10139 10131 5.0 6.0 5.0
## 1767 101893 10139 10132 6.0 5.0 6.0
## 1768 101894 10139 10133 7.0 NA NA
## 1769 101896 10140 10156 10.0 8.0 8.0
## 1770 101897 10140 10157 7.0 7.0 7.0
## 1771 101898 10140 10158 6.0 9.0 8.0
## 1772 101899 10140 10159 8.0 7.0 8.0
## 1773 101900 10140 10160 6.0 7.0 7.0
## 1774 101901 10140 10161 7.0 7.0 7.0
## 1775 101902 10140 10162 8.0 7.0 9.0
## 1776 101903 10140 10163 8.0 8.0 9.0
## 1777 101904 10140 10164 4.0 4.0 8.0
## 1778 101905 10140 10165 7.0 6.0 7.0
## 1779 101906 10140 10166 8.0 8.0 7.0
## 1780 101907 10140 10167 8.0 8.0 9.0
## 1781 101908 10140 10168 7.0 7.0 7.0
## 1782 101909 10140 10169 7.0 8.0 9.0
## 1783 101910 10140 10170 8.0 7.0 6.0
## 1784 101911 10140 10171 8.0 7.0 7.0
## 1785 101912 10141 10156 6.0 8.0 9.0
## 1786 101913 10141 10157 5.0 7.0 7.0
## 1787 101914 10141 10158 4.0 9.0 7.0
## 1788 101915 10141 10159 5.0 10.0 8.0
## 1789 101916 10141 10160 3.0 6.0 6.0
## 1790 101917 10141 10161 5.0 8.0 7.0
## 1791 101918 10141 10162 4.0 9.0 9.0
## 1792 101919 10141 10163 6.0 9.0 9.0
## 1793 101920 10141 10164 3.0 7.0 7.0
## 1794 101921 10141 10165 3.0 7.0 6.0
## 1795 101922 10141 10166 5.0 5.0 5.0
## 1796 101923 10141 10167 7.0 9.0 9.0
## 1797 101924 10141 10168 5.0 7.0 7.0
## 1798 101925 10141 10169 5.0 8.0 9.0
## 1799 101926 10141 10170 NA 8.0 9.0
## 1800 101927 10141 10171 4.0 6.0 5.0
## 1801 101928 10142 10156 9.0 9.0 9.0
## 1802 101929 10142 10157 8.0 8.0 8.0
## 1803 101930 10142 10158 6.0 8.0 9.0
## 1804 101931 10142 10159 6.0 8.0 10.0
## 1805 101932 10142 10160 8.0 7.0 7.0
## 1806 101933 10142 10161 8.0 8.0 8.0
## 1807 101934 10142 10162 7.0 7.0 9.0
## 1808 101935 10142 10163 7.0 8.0 10.0
## 1809 101936 10142 10164 7.0 7.0 9.0
## 1810 101937 10142 10165 4.0 6.0 5.0
## 1811 101938 10142 10166 8.0 8.0 8.0
## 1812 101939 10142 10167 8.0 7.0 8.0
## 1813 101940 10142 10168 NA NA NA
## 1814 101941 10142 10169 8.0 9.0 9.0
## 1815 101942 10142 10170 4.0 7.0 8.0
## 1816 101943 10142 10171 5.0 7.0 6.0
## 1817 101944 10143 10156 9.0 8.0 9.0
## 1818 101945 10143 10157 8.0 8.0 8.0
## 1819 101946 10143 10158 4.0 8.0 9.0
## 1820 101947 10143 10159 6.0 8.0 10.0
## 1821 101948 10143 10160 5.0 6.0 7.0
## 1822 101949 10143 10161 6.0 7.0 6.0
## 1823 101950 10143 10162 4.0 8.0 9.0
## 1824 101951 10143 10163 6.0 8.0 9.0
## 1825 101952 10143 10164 5.0 7.0 7.0
## 1826 101953 10143 10165 2.0 6.0 6.0
## 1827 101954 10143 10166 7.0 7.0 7.0
## 1828 101955 10143 10167 7.0 9.0 9.0
## 1829 101956 10143 10168 6.0 7.0 6.0
## 1830 101957 10143 10169 6.0 8.0 9.0
## 1831 101958 10143 10170 2.0 7.0 9.0
## 1832 101959 10143 10171 4.0 6.0 6.0
## 1833 101960 10144 10156 8.0 9.0 8.0
## 1834 101961 10144 10157 6.0 8.0 8.0
## 1835 101962 10144 10158 5.0 8.0 NA
## 1836 101963 10144 10159 5.0 6.0 7.0
## 1837 101964 10144 10160 4.0 6.0 8.0
## 1838 101965 10144 10161 5.0 8.0 7.0
## 1839 101966 10144 10162 9.0 10.0 10.0
## 1840 101967 10144 10163 8.0 7.0 9.0
## 1841 101968 10144 10164 7.0 7.0 7.0
## 1842 101969 10144 10165 3.0 6.0 8.0
## 1843 101970 10144 10166 4.0 6.0 7.0
## 1844 101971 10144 10167 7.0 8.0 9.0
## 1845 101972 10144 10168 5.0 7.0 7.0
## 1846 101973 10144 10169 6.0 8.0 9.0
## 1847 101974 10144 10170 1.0 4.0 5.0
## 1848 101975 10144 10171 5.0 6.0 5.0
## 1849 101976 10145 10156 9.0 7.0 7.0
## 1850 101977 10145 10157 6.0 6.0 6.0
## 1851 101978 10145 10158 8.0 10.0 9.0
## 1852 101979 10145 10159 7.0 8.0 9.0
## 1853 101980 10145 10160 5.0 7.0 7.0
## 1854 101981 10145 10161 7.0 9.0 7.0
## 1855 101982 10145 10162 9.0 6.0 6.0
## 1856 101983 10145 10163 7.0 8.0 8.0
## 1857 101984 10145 10164 8.0 8.0 8.0
## 1858 101985 10145 10165 3.0 6.0 5.0
## 1859 101986 10145 10166 4.0 7.0 8.0
## 1860 101987 10145 10167 10.0 8.0 8.0
## 1861 101988 10145 10168 7.0 7.0 7.0
## 1862 101989 10145 10169 5.0 6.0 7.0
## 1863 101990 10145 10170 8.0 4.0 NA
## 1864 101991 10145 10171 5.0 7.0 5.0
## 1865 101992 10146 10156 9.0 8.0 8.0
## 1866 101993 10146 10157 7.0 6.0 6.0
## 1867 101994 10146 10158 7.0 8.0 8.0
## 1868 101995 10146 10159 6.0 6.0 8.0
## 1869 101996 10146 10160 6.0 6.0 7.0
## 1870 101997 10146 10161 8.0 8.0 8.0
## 1871 101998 10146 10162 7.0 7.0 7.0
## 1872 101999 10146 10163 8.0 8.0 9.0
## 1873 102000 10146 10164 8.0 8.0 8.0
## 1874 102001 10146 10165 6.0 5.0 6.0
## 1875 102002 10146 10166 7.0 8.0 8.0
## 1876 102003 10146 10167 9.0 9.0 9.0
## 1877 102004 10146 10168 6.0 6.0 7.0
## 1878 102005 10146 10169 7.0 7.0 7.0
## 1879 102006 10146 10170 7.0 4.0 6.0
## 1880 102007 10146 10171 5.0 8.0 5.0
## 1881 102008 10147 10156 9.0 9.0 9.0
## 1882 102009 10147 10157 8.0 9.0 7.0
## 1883 102010 10147 10158 6.0 9.0 9.0
## 1884 102011 10147 10159 7.0 6.0 7.0
## 1885 102012 10147 10160 8.0 6.0 6.0
## 1886 102013 10147 10161 8.0 8.0 8.0
## 1887 102014 10147 10162 6.0 6.0 6.0
## 1888 102015 10147 10163 9.0 7.0 9.0
## 1889 102016 10147 10164 7.0 7.0 7.0
## 1890 102017 10147 10165 6.0 6.0 6.0
## 1891 102018 10147 10166 5.0 6.0 6.0
## 1892 102019 10147 10167 9.0 8.0 9.0
## 1893 102020 10147 10168 7.0 7.0 7.0
## 1894 102021 10147 10169 8.0 7.0 8.0
## 1895 102022 10147 10170 6.0 4.0 7.0
## 1896 102023 10147 10171 8.0 8.0 8.0
## 1897 102024 10148 10156 10.0 8.0 9.0
## 1898 102025 10148 10157 8.0 9.0 9.0
## 1899 102026 10148 10158 7.0 10.0 9.0
## 1900 102027 10148 10159 8.0 8.0 10.0
## 1901 102028 10148 10160 6.0 9.0 8.0
## 1902 102029 10148 10161 9.0 9.0 9.0
## 1903 102030 10148 10162 9.0 10.0 9.0
## 1904 102031 10148 10163 8.0 10.0 9.0
## 1905 102032 10148 10164 5.0 7.0 9.0
## 1906 102033 10148 10165 7.0 5.0 6.0
## 1907 102034 10148 10166 8.0 7.0 7.0
## 1908 102035 10148 10167 7.0 8.0 9.0
## 1909 102036 10148 10168 6.0 7.0 7.0
## 1910 102037 10148 10169 7.0 9.0 9.0
## 1911 102038 10148 10170 4.0 6.0 8.0
## 1912 102039 10148 10171 7.0 7.0 7.0
## 1913 102040 10149 10156 10.0 8.0 8.0
## 1914 102041 10149 10157 8.0 8.0 7.0
## 1915 102042 10149 10158 8.0 8.0 NA
## 1916 102043 10149 10159 10.0 8.0 8.0
## 1917 102044 10149 10160 8.0 7.0 7.0
## 1918 102045 10149 10161 8.0 9.0 8.0
## 1919 102046 10149 10162 9.0 9.0 7.0
## 1920 102047 10149 10163 10.0 9.0 8.0
## 1921 102048 10149 10164 8.0 8.0 8.0
## 1922 102049 10149 10165 8.0 8.0 5.0
## 1923 102050 10149 10166 9.0 8.0 7.0
## 1924 102051 10149 10167 9.0 9.0 9.0
## 1925 102052 10149 10168 8.0 7.0 7.0
## 1926 102053 10149 10169 9.0 9.0 8.0
## 1927 102054 10149 10170 8.0 7.0 8.0
## 1928 102055 10149 10171 9.0 8.0 7.0
## 1929 102056 10150 10156 9.0 9.0 8.0
## 1930 102057 10150 10157 7.0 7.0 7.0
## 1931 102058 10150 10158 7.0 9.0 10.0
## 1932 102059 10150 10159 7.0 10.0 9.0
## 1933 102060 10150 10160 6.0 7.0 7.0
## 1934 102061 10150 10161 6.0 8.0 8.0
## 1935 102062 10150 10162 8.0 9.0 9.0
## 1936 102063 10150 10163 8.0 9.0 10.0
## 1937 102064 10150 10164 8.0 8.0 NA
## 1938 102065 10150 10165 7.0 8.0 8.0
## 1939 102066 10150 10166 7.0 7.0 7.0
## 1940 102067 10150 10167 10.0 9.0 8.0
## 1941 102068 10150 10168 5.0 7.0 6.0
## 1942 102069 10150 10169 7.0 7.0 8.0
## 1943 102070 10150 10170 7.0 8.0 8.0
## 1944 102071 10150 10171 5.0 5.0 5.0
## 1945 102072 10151 10156 7.0 9.0 8.0
## 1946 102073 10151 10157 7.0 7.0 7.0
## 1947 102074 10151 10158 7.0 9.0 8.0
## 1948 102075 10151 10159 7.0 9.0 6.0
## 1949 102076 10151 10160 5.0 7.0 6.0
## 1950 102077 10151 10161 8.0 10.0 8.0
## 1951 102078 10151 10162 7.0 8.0 8.0
## 1952 102079 10151 10163 7.0 8.0 8.0
## 1953 102080 10151 10164 6.0 7.0 7.0
## 1954 102081 10151 10165 4.0 8.0 6.0
## 1955 102082 10151 10166 7.0 7.0 7.0
## 1956 102083 10151 10167 9.0 10.0 8.0
## 1957 102084 10151 10168 5.0 7.0 7.0
## 1958 102085 10151 10169 6.0 8.0 8.0
## 1959 102086 10151 10170 4.0 6.0 8.0
## 1960 102087 10151 10171 5.0 5.0 5.0
## 1961 102088 10152 10156 9.0 9.0 7.0
## 1962 102089 10152 10157 7.0 8.0 7.0
## 1963 102090 10152 10158 8.0 8.0 8.0
## 1964 102091 10152 10159 9.0 9.0 8.0
## 1965 102092 10152 10160 7.0 6.0 7.0
## 1966 102093 10152 10161 8.0 10.0 8.0
## 1967 102094 10152 10162 8.0 7.0 7.0
## 1968 102095 10152 10163 10.0 8.0 8.0
## 1969 102096 10152 10164 7.0 7.0 7.0
## 1970 102097 10152 10165 5.0 7.0 7.0
## 1971 102098 10152 10166 8.0 8.0 8.0
## 1972 102099 10152 10167 10.0 9.0 9.0
## 1973 102100 10152 10168 6.0 6.0 7.0
## 1974 102101 10152 10169 9.0 7.0 8.0
## 1975 102102 10152 10170 7.0 8.0 8.0
## 1976 102103 10152 10171 6.0 7.0 6.0
## 1977 102104 10153 10156 10.0 9.0 9.0
## 1978 102105 10153 10157 7.0 9.0 8.0
## 1979 102106 10153 10158 7.0 8.0 8.0
## 1980 102107 10153 10159 7.0 8.0 8.0
## 1981 102108 10153 10160 6.0 5.0 3.0
## 1982 102109 10153 10161 8.0 6.0 6.0
## 1983 102110 10153 10162 5.0 10.0 8.0
## 1984 102111 10153 10163 9.0 9.0 8.0
## 1985 102112 10153 10164 7.0 7.0 7.0
## 1986 102113 10153 10165 6.0 6.0 5.0
## 1987 102114 10153 10166 7.0 7.0 7.0
## 1988 102115 10153 10167 9.0 8.0 8.0
## 1989 102116 10153 10168 6.0 7.0 7.0
## 1990 102117 10153 10169 10.0 8.0 8.0
## 1991 102118 10153 10170 5.0 6.0 6.0
## 1992 102119 10153 10171 6.0 7.0 7.0
## 1993 102120 10154 10156 9.0 8.0 8.0
## 1994 102121 10154 10157 6.0 7.0 7.0
## 1995 102122 10154 10158 8.0 10.0 8.0
## 1996 102123 10154 10159 7.0 9.0 10.0
## 1997 102124 10154 10160 5.0 7.0 7.0
## 1998 102125 10154 10161 8.0 9.0 9.0
## 1999 102126 10154 10162 8.0 8.0 10.0
## 2000 102127 10154 10163 7.0 8.0 7.0
## 2001 102128 10154 10164 7.0 7.0 7.0
## 2002 102129 10154 10165 7.0 6.0 6.0
## 2003 102130 10154 10166 7.0 8.0 8.0
## 2004 102131 10154 10167 8.0 8.0 10.0
## 2005 102132 10154 10168 6.0 8.0 8.0
## 2006 102133 10154 10169 6.0 8.0 9.0
## 2007 102134 10154 10170 6.0 8.0 7.0
## 2008 102135 10154 10171 7.0 7.0 7.0
## 2009 102136 10155 10156 9.0 8.0 8.0
## 2010 102137 10155 10157 6.0 10.0 6.0
## 2011 102138 10155 10158 10.0 8.0 8.0
## 2012 102139 10155 10159 8.0 8.0 8.0
## 2013 102140 10155 10160 5.0 5.0 6.0
## 2014 102141 10155 10161 7.0 6.0 6.0
## 2015 102142 10155 10162 8.0 7.0 8.0
## 2016 102143 10155 10163 9.0 7.0 8.0
## 2017 102144 10155 10164 8.0 8.0 8.0
## 2018 102145 10155 10165 6.0 7.0 6.0
## 2019 102146 10155 10166 6.0 8.0 8.0
## 2020 102147 10155 10167 10.0 8.0 8.0
## 2021 102148 10155 10168 6.0 6.0 6.0
## 2022 102149 10155 10169 9.0 8.0 9.0
## 2023 102150 10155 10170 8.0 5.0 7.0
## 2024 102151 10155 10171 7.0 8.0 8.0
## 2025 102152 10156 10140 4.0 5.0 6.0
## 2026 102153 10156 10141 5.0 6.0 8.0
## 2027 102154 10156 10142 4.0 6.0 7.0
## 2028 102155 10156 10143 7.0 10.0 10.0
## 2029 102156 10156 10144 4.0 6.0 8.0
## 2030 102157 10156 10145 5.0 5.0 8.0
## 2031 102158 10156 10146 4.0 8.0 8.0
## 2032 102159 10156 10147 6.0 7.0 7.0
## 2033 102160 10156 10148 5.0 5.0 8.0
## 2034 102161 10156 10149 4.0 5.0 5.0
## 2035 102162 10156 10150 6.0 7.0 7.0
## 2036 102163 10156 10151 6.0 6.0 8.0
## 2037 102164 10156 10152 5.0 8.0 10.0
## 2038 102165 10156 10153 2.0 6.0 7.0
## 2039 102166 10156 10154 4.0 7.0 10.0
## 2040 102167 10156 10155 2.0 3.0 3.0
## 2041 102168 10157 10140 5.0 10.0 9.0
## 2042 102169 10157 10141 4.0 6.0 8.0
## 2043 102170 10157 10142 4.0 7.0 8.0
## 2044 102171 10157 10143 7.0 10.0 10.0
## 2045 102172 10157 10144 5.0 7.0 10.0
## 2046 102173 10157 10145 5.0 6.0 8.0
## 2047 102174 10157 10146 3.0 8.0 8.0
## 2048 102175 10157 10147 4.0 7.0 8.0
## 2049 102176 10157 10148 5.0 8.0 10.0
## 2050 102177 10157 10149 3.0 7.0 7.0
## 2051 102178 10157 10150 4.0 6.0 5.0
## 2052 102179 10157 10151 5.0 5.0 6.0
## 2053 102180 10157 10152 6.0 8.0 9.0
## 2054 102181 10157 10153 1.0 4.0 6.0
## 2055 102182 10157 10154 3.0 10.0 10.0
## 2056 102183 10157 10155 2.0 6.0 6.0
## 2057 102184 10158 10140 8.0 8.0 8.0
## 2058 102185 10158 10141 5.0 5.0 7.0
## 2059 102186 10158 10142 7.0 8.0 8.0
## 2060 102187 10158 10143 8.0 10.0 7.0
## 2061 102188 10158 10144 8.0 10.0 7.0
## 2062 102189 10158 10145 8.0 6.0 7.0
## 2063 102190 10158 10146 7.0 5.0 7.0
## 2064 102191 10158 10147 9.0 7.0 8.0
## 2065 102192 10158 10148 9.0 7.0 7.0
## 2066 102193 10158 10149 8.0 6.0 8.0
## 2067 102194 10158 10150 6.0 8.0 7.0
## 2068 102195 10158 10151 5.0 6.0 6.0
## 2069 102196 10158 10152 9.0 9.0 9.0
## 2070 102197 10158 10153 7.0 7.0 6.0
## 2071 102198 10158 10154 7.0 10.0 10.0
## 2072 102199 10158 10155 7.0 6.0 7.0
## 2073 102200 10159 10140 10.0 10.0 9.0
## 2074 102201 10159 10141 5.0 6.0 8.0
## 2075 102202 10159 10142 8.0 8.0 8.0
## 2076 102203 10159 10143 10.0 9.0 9.0
## 2077 102204 10159 10144 6.0 8.0 8.0
## 2078 102205 10159 10145 7.0 6.0 7.0
## 2079 102206 10159 10146 5.0 8.0 8.0
## 2080 102207 10159 10147 8.0 6.0 8.0
## 2081 102208 10159 10148 8.0 5.0 8.0
## 2082 102209 10159 10149 9.0 7.0 8.0
## 2083 102210 10159 10150 8.0 8.0 8.0
## 2084 102211 10159 10151 7.0 7.0 7.0
## 2085 102212 10159 10152 10.0 8.0 9.0
## 2086 102213 10159 10153 8.0 7.0 7.0
## 2087 102214 10159 10154 8.0 9.0 10.0
## 2088 102215 10159 10155 7.0 7.0 6.0
## 2089 102216 10160 10140 8.0 10.0 9.0
## 2090 102217 10160 10141 6.0 7.0 9.0
## 2091 102218 10160 10142 7.0 7.0 8.0
## 2092 102219 10160 10143 9.0 9.0 9.0
## 2093 102220 10160 10144 5.0 5.0 10.0
## 2094 102221 10160 10145 5.0 6.0 6.0
## 2095 102222 10160 10146 6.0 8.0 8.0
## 2096 102223 10160 10147 4.0 8.0 7.0
## 2097 102224 10160 10148 6.0 6.0 7.0
## 2098 102225 10160 10149 9.0 8.0 7.0
## 2099 102226 10160 10150 5.0 6.0 8.0
## 2100 102227 10160 10151 5.0 5.0 7.0
## 2101 102228 10160 10152 5.0 8.0 8.0
## 2102 102229 10160 10153 2.0 4.0 6.0
## 2103 102230 10160 10154 7.0 6.0 10.0
## 2104 102231 10160 10155 3.0 5.0 7.0
## 2105 102232 10161 10140 6.0 8.0 8.0
## 2106 102233 10161 10141 9.0 8.0 9.0
## 2107 102234 10161 10142 6.0 7.0 7.0
## 2108 102235 10161 10143 9.0 9.0 8.0
## 2109 102236 10161 10144 6.0 8.0 8.0
## 2110 102237 10161 10145 7.0 8.0 8.0
## 2111 102238 10161 10146 7.0 9.0 9.0
## 2112 102239 10161 10147 5.0 6.0 6.0
## 2113 102240 10161 10148 7.0 7.0 8.0
## 2114 102241 10161 10149 7.0 5.0 7.0
## 2115 102242 10161 10150 7.0 8.0 7.0
## 2116 102243 10161 10151 8.0 8.0 8.0
## 2117 102244 10161 10152 9.0 9.0 8.0
## 2118 102245 10161 10153 6.0 7.0 6.0
## 2119 102246 10161 10154 6.0 9.0 10.0
## 2120 102247 10161 10155 5.0 7.0 6.0
## 2121 102248 10162 10140 8.0 9.0 10.0
## 2122 102249 10162 10141 8.0 7.0 9.0
## 2123 102250 10162 10142 8.0 8.0 7.0
## 2124 102251 10162 10143 9.0 10.0 10.0
## 2125 102252 10162 10144 10.0 9.0 10.0
## 2126 102253 10162 10145 6.0 8.0 8.0
## 2127 102254 10162 10146 4.0 6.0 7.0
## 2128 102255 10162 10147 3.0 7.0 8.0
## 2129 102256 10162 10148 8.0 7.0 6.0
## 2130 102257 10162 10149 6.0 8.0 8.0
## 2131 102258 10162 10150 6.0 8.0 8.0
## 2132 102259 10162 10151 6.0 6.0 7.0
## 2133 102260 10162 10152 7.0 9.0 9.0
## 2134 102261 10162 10153 6.0 8.0 7.0
## 2135 102262 10162 10154 7.0 9.0 10.0
## 2136 102263 10162 10155 5.0 6.0 6.0
## 2137 102264 10163 10140 6.0 8.0 7.0
## 2138 102265 10163 10141 10.0 9.0 9.0
## 2139 102266 10163 10142 7.0 6.0 6.0
## 2140 102267 10163 10143 8.0 8.0 7.0
## 2141 102268 10163 10144 8.0 8.0 7.0
## 2142 102269 10163 10145 7.0 7.0 7.0
## 2143 102270 10163 10146 6.0 7.0 6.0
## 2144 102271 10163 10147 8.0 7.0 6.0
## 2145 102272 10163 10148 9.0 8.0 8.0
## 2146 102273 10163 10149 8.0 5.0 5.0
## 2147 102274 10163 10150 8.0 8.0 7.0
## 2148 102275 10163 10151 5.0 5.0 5.0
## 2149 102276 10163 10152 7.0 9.0 7.0
## 2150 102277 10163 10153 7.0 6.0 5.0
## 2151 102278 10163 10154 8.0 5.0 10.0
## 2152 102279 10163 10155 6.0 7.0 7.0
## 2153 102280 10164 10140 8.0 6.0 6.0
## 2154 102281 10164 10141 9.0 8.0 9.0
## 2155 102282 10164 10142 7.0 8.0 8.0
## 2156 102283 10164 10143 7.0 7.0 7.0
## 2157 102284 10164 10144 5.0 8.0 6.0
## 2158 102285 10164 10145 8.0 8.0 8.0
## 2159 102286 10164 10146 7.0 8.0 7.0
## 2160 102287 10164 10147 8.0 5.0 6.0
## 2161 102288 10164 10148 6.0 5.0 6.0
## 2162 102289 10164 10149 6.0 4.0 6.0
## 2163 102290 10164 10150 5.0 5.0 6.0
## 2164 102291 10164 10151 4.0 4.0 5.0
## 2165 102292 10164 10152 9.0 9.0 9.0
## 2166 102293 10164 10153 8.0 7.0 7.0
## 2167 102294 10164 10154 7.0 6.0 10.0
## 2168 102295 10164 10155 6.0 7.0 7.0
## 2169 102296 10165 10140 8.0 8.0 8.0
## 2170 102297 10165 10141 5.0 6.0 7.0
## 2171 102298 10165 10142 6.0 7.0 8.0
## 2172 102299 10165 10143 10.0 9.0 9.0
## 2173 102300 10165 10144 5.0 7.0 8.0
## 2174 102301 10165 10145 6.0 6.0 6.0
## 2175 102302 10165 10146 3.0 8.0 8.0
## 2176 102303 10165 10147 4.0 6.0 7.0
## 2177 102304 10165 10148 7.0 7.0 8.0
## 2178 102305 10165 10149 6.0 3.0 6.0
## 2179 102306 10165 10150 7.0 9.0 8.0
## 2180 102307 10165 10151 5.0 6.0 7.0
## 2181 102308 10165 10152 8.0 8.0 8.0
## 2182 102309 10165 10153 5.0 5.0 6.0
## 2183 102310 10165 10154 6.0 9.0 10.0
## 2184 102311 10165 10155 4.0 5.0 5.0
## 2185 102312 10166 10140 6.0 9.0 9.0
## 2186 102313 10166 10141 7.0 8.0 9.0
## 2187 102314 10166 10142 5.0 5.0 6.0
## 2188 102315 10166 10143 6.0 9.0 8.0
## 2189 102316 10166 10144 6.0 8.0 10.0
## 2190 102317 10166 10145 4.0 8.0 8.0
## 2191 102318 10166 10146 2.0 8.0 8.0
## 2192 102319 10166 10147 4.0 7.0 8.0
## 2193 102320 10166 10148 7.0 8.0 8.0
## 2194 102321 10166 10149 6.0 3.0 7.0
## 2195 102322 10166 10150 4.0 7.0 7.0
## 2196 102323 10166 10151 5.0 6.0 6.0
## 2197 102324 10166 10152 5.0 9.0 7.0
## 2198 102325 10166 10153 3.0 6.0 4.0
## 2199 102326 10166 10154 6.0 9.0 10.0
## 2200 102327 10166 10155 2.0 8.0 8.0
## 2201 102328 10167 10140 10.0 10.0 10.0
## 2202 102329 10167 10141 8.0 9.0 9.0
## 2203 102330 10167 10142 7.0 7.0 8.0
## 2204 102331 10167 10143 9.0 7.0 9.0
## 2205 102332 10167 10144 8.0 8.0 8.0
## 2206 102333 10167 10145 8.0 7.0 8.0
## 2207 102334 10167 10146 8.0 8.0 8.0
## 2208 102335 10167 10147 8.0 7.0 8.0
## 2209 102336 10167 10148 9.0 9.0 9.0
## 2210 102337 10167 10149 7.0 8.0 7.0
## 2211 102338 10167 10150 8.0 9.0 9.0
## 2212 102339 10167 10151 7.0 7.0 7.0
## 2213 102340 10167 10152 9.0 9.0 9.0
## 2214 102341 10167 10153 8.0 8.0 8.0
## 2215 102342 10167 10154 7.0 9.0 10.0
## 2216 102343 10167 10155 8.0 7.0 7.0
## 2217 102344 10168 10140 4.0 7.0 7.0
## 2218 102345 10168 10141 5.0 5.0 9.0
## 2220 102347 10168 10143 8.0 10.0 9.0
## 2221 102348 10168 10144 6.0 8.0 8.0
## 2222 102349 10168 10145 4.0 7.0 8.0
## 2223 102350 10168 10146 3.0 8.0 8.0
## 2224 102351 10168 10147 4.0 8.0 7.0
## 2225 102352 10168 10148 5.0 6.0 9.0
## 2226 102353 10168 10149 3.0 8.0 8.0
## 2227 102354 10168 10150 4.0 6.0 7.0
## 2228 102355 10168 10151 5.0 5.0 7.0
## 2229 102356 10168 10152 6.0 10.0 10.0
## 2230 102357 10168 10153 2.0 8.0 8.0
## 2231 102358 10168 10154 4.0 8.0 10.0
## 2232 102359 10168 10155 3.0 6.0 7.0
## 2233 102360 10169 10140 4.0 10.0 9.0
## 2234 102361 10169 10141 8.0 9.0 9.0
## 2235 102362 10169 10142 5.0 7.0 8.0
## 2236 102363 10169 10143 9.0 9.0 9.0
## 2237 102364 10169 10144 8.0 8.0 8.0
## 2238 102365 10169 10145 4.0 7.0 9.0
## 2239 102366 10169 10146 4.0 8.0 8.0
## 2240 102367 10169 10147 6.0 8.0 8.0
## 2241 102368 10169 10148 6.0 7.0 9.0
## 2242 102369 10169 10149 5.0 7.0 6.0
## 2243 102370 10169 10150 6.0 7.0 8.0
## 2244 102371 10169 10151 6.0 6.0 7.0
## 2245 102372 10169 10152 8.0 9.0 9.0
## 2246 102373 10169 10153 4.0 7.0 7.0
## 2247 102374 10169 10154 5.0 8.0 10.0
## 2248 102375 10169 10155 5.0 7.0 7.0
## 2249 102376 10170 10140 8.0 7.0 8.0
## 2250 102377 10170 10141 8.0 8.0 9.0
## 2251 102378 10170 10142 5.0 8.0 7.0
## 2252 102379 10170 10143 9.0 9.0 9.0
## 2253 102380 10170 10144 8.0 8.0 8.0
## 2254 102381 10170 10145 8.0 10.0 10.0
## 2255 102382 10170 10146 7.0 8.0 8.0
## 2256 102383 10170 10147 7.0 7.0 7.0
## 2257 102384 10170 10148 6.0 7.0 8.0
## 2258 102385 10170 10149 7.0 8.0 7.0
## 2259 102386 10170 10150 6.0 8.0 7.0
## 2260 102387 10170 10151 6.0 5.0 6.0
## 2261 102388 10170 10152 9.0 10.0 10.0
## 2262 102389 10170 10153 5.0 8.0 8.0
## 2263 102390 10170 10154 5.0 9.0 10.0
## 2264 102391 10170 10155 7.0 7.0 7.0
## 2265 102392 10171 10140 6.0 4.0 8.0
## 2266 102393 10171 10141 7.0 5.0 8.0
## 2267 102394 10171 10142 6.0 7.0 8.0
## 2268 102395 10171 10143 8.0 10.0 10.0
## 2269 102396 10171 10144 6.0 8.0 8.0
## 2270 102397 10171 10145 6.0 8.0 8.0
## 2271 102398 10171 10146 7.0 9.0 9.0
## 2272 102399 10171 10147 5.0 8.0 8.0
## 2273 102400 10171 10148 6.0 8.0 8.0
## 2274 102401 10171 10149 8.0 7.0 8.0
## 2275 102402 10171 10150 5.0 8.0 8.0
## 2276 102403 10171 10151 5.0 5.0 6.0
## 2277 102404 10171 10152 9.0 9.0 10.0
## 2278 102405 10171 10153 3.0 8.0 8.0
## 2279 102406 10171 10154 6.0 9.0 10.0
## 2280 102407 10171 10155 5.0 6.0 5.0
## 2281 102408 10172 10182 8.0 10.0 10.0
## 2282 102409 10172 10183 6.0 6.0 7.0
## 2283 102410 10172 10184 4.0 6.0 6.0
## 2284 102411 10172 10185 6.0 8.0 NA
## 2285 102412 10172 10186 4.0 5.0 5.0
## 2286 102413 10172 10187 5.0 8.0 7.0
## 2287 102414 10172 10188 5.0 8.0 8.0
## 2288 102415 10172 10189 5.0 8.0 7.0
## 2289 102416 10172 10190 3.0 8.0 8.0
## 2290 102417 10172 10191 3.0 9.0 7.0
## 2291 102418 10173 10182 7.0 10.0 10.0
## 2292 102419 10173 10183 3.0 8.0 8.0
## 2293 102420 10173 10184 7.0 6.0 6.0
## 2294 102421 10173 10185 6.0 NA 10.0
## 2295 102422 10173 10186 6.0 5.0 6.0
## 2296 102423 10173 10187 6.0 6.0 5.0
## 2297 102424 10173 10188 9.0 8.0 8.0
## 2298 102425 10173 10189 5.0 9.0 8.0
## 2299 102426 10173 10190 4.0 8.0 7.0
## 2300 102427 10173 10191 5.0 8.0 8.0
## 2301 102428 10174 10182 10.0 10.0 10.0
## 2302 102429 10174 10183 3.0 8.0 8.0
## 2303 102430 10174 10184 8.0 7.0 7.0
## 2304 102431 10174 10185 NA NA NA
## 2305 102432 10174 10186 6.0 5.0 7.0
## 2306 102433 10174 10187 8.0 8.0 5.0
## 2307 102434 10174 10188 9.0 NA 8.0
## 2308 102435 10174 10189 7.0 8.0 7.0
## 2309 102436 10174 10190 7.0 8.0 9.0
## 2310 102437 10174 10191 6.0 9.0 9.0
## 2311 102438 10175 10182 8.0 10.0 10.0
## 2312 102439 10175 10183 2.0 9.0 9.0
## 2313 102440 10175 10184 5.0 5.0 7.0
## 2314 102441 10175 10185 4.0 NA NA
## 2315 102442 10175 10186 3.0 4.0 5.0
## 2316 102443 10175 10187 5.0 6.0 6.0
## 2317 102444 10175 10188 5.0 NA 5.0
## 2318 102445 10175 10189 5.0 7.0 7.0
## 2319 102446 10175 10190 6.0 8.0 6.0
## 2320 102447 10175 10191 6.0 8.0 8.0
## 2321 102448 10176 10182 8.0 10.0 10.0
## 2322 102449 10176 10183 3.0 7.0 7.0
## 2323 102450 10176 10184 5.0 6.0 6.0
## 2325 102452 10176 10186 5.0 5.0 6.0
## 2326 102453 10176 10187 5.0 8.0 8.0
## 2327 102454 10176 10188 3.0 9.0 8.0
## 2328 102455 10176 10189 5.0 8.0 7.0
## 2329 102456 10176 10190 4.0 7.0 6.0
## 2330 102457 10176 10191 3.0 9.0 9.0
## 2331 102458 10177 10182 10.0 10.0 10.0
## 2332 102459 10177 10183 8.0 8.0 9.0
## 2333 102460 10177 10184 7.0 7.0 7.0
## 2334 102461 10177 10185 7.0 7.0 7.0
## 2335 102462 10177 10186 7.0 6.0 6.0
## 2336 102463 10177 10187 7.0 8.0 8.0
## 2337 102464 10177 10188 10.0 8.0 10.0
## 2338 102465 10177 10189 8.0 8.0 9.0
## 2339 102466 10177 10190 7.0 7.0 8.0
## 2340 102467 10177 10191 9.0 9.0 9.0
## 2341 102468 10178 10182 10.0 9.0 8.0
## 2342 102469 10178 10183 9.0 10.0 8.0
## 2343 102470 10178 10184 7.0 7.0 NA
## 2344 102471 10178 10185 NA NA 7.0
## 2345 102472 10178 10186 8.0 8.0 5.0
## 2346 102473 10178 10187 6.0 8.0 8.0
## 2347 102474 10178 10188 7.0 5.0 7.0
## 2348 102475 10178 10189 7.0 8.0 7.0
## 2349 102476 10178 10190 7.0 7.0 8.0
## 2350 102477 10178 10191 7.0 8.0 8.0
## 2351 102478 10179 10182 10.0 10.0 10.0
## 2352 102479 10179 10183 9.0 10.0 8.0
## 2353 102480 10179 10184 6.0 8.0 8.0
## 2354 102481 10179 10185 NA NA NA
## 2355 102482 10179 10186 6.0 7.0 5.0
## 2356 102483 10179 10187 6.0 7.0 7.0
## 2357 102484 10179 10188 6.0 9.0 NA
## 2358 102485 10179 10189 8.0 8.0 8.0
## 2359 102486 10179 10190 7.0 7.0 6.0
## 2360 102487 10179 10191 8.0 10.0 8.0
## 2361 102488 10180 10182 8.0 10.0 10.0
## 2362 102489 10180 10183 6.0 8.0 9.0
## 2363 102490 10180 10184 5.0 7.0 6.0
## 2364 102491 10180 10185 NA NA NA
## 2365 102492 10180 10186 5.0 7.0 7.0
## 2366 102493 10180 10187 5.0 7.0 6.0
## 2367 102494 10180 10188 7.0 5.0 7.0
## 2368 102495 10180 10189 5.0 7.0 7.0
## 2369 102496 10180 10190 3.0 8.0 7.0
## 2370 102497 10180 10191 3.0 9.0 7.0
## 2371 102498 10181 10182 10.0 10.0 10.0
## 2372 102499 10181 10183 6.0 6.0 6.0
## 2373 102500 10181 10184 7.0 7.0 6.0
## 2374 102501 10181 10185 4.0 NA NA
## 2375 102502 10181 10186 4.0 5.0 5.0
## 2376 102503 10181 10187 6.0 8.0 7.0
## 2377 102504 10181 10188 8.0 3.0 8.0
## 2378 102505 10181 10189 5.0 7.0 7.0
## 2379 102506 10181 10190 5.0 7.0 6.0
## 2380 102507 10181 10191 6.0 8.0 7.0
## 2381 102508 10182 10172 5.0 6.0 6.0
## 2382 102509 10182 10173 7.0 7.0 7.0
## 2383 102510 10182 10174 7.0 7.0 8.0
## 2384 102511 10182 10175 5.0 7.0 7.0
## 2385 102512 10182 10176 5.0 7.0 7.0
## 2386 102513 10182 10177 3.0 7.0 6.0
## 2387 102514 10182 10178 7.0 8.0 7.0
## 2388 102515 10182 10179 8.0 9.0 8.0
## 2389 102516 10182 10180 7.0 8.0 8.0
## 2390 102517 10182 10181 8.0 9.0 9.0
## 2391 102518 10183 10172 5.0 5.0 5.0
## 2392 102519 10183 10173 2.0 7.0 7.0
## 2393 102520 10183 10174 6.0 5.0 6.0
## 2394 102521 10183 10175 2.0 3.0 3.0
## 2395 102522 10183 10176 7.0 7.0 7.0
## 2396 102523 10183 10177 4.0 6.0 6.0
## 2397 102524 10183 10178 4.0 4.0 4.0
## 2398 102525 10183 10179 6.0 8.0 9.0
## 2399 102526 10183 10180 7.0 7.0 8.0
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## 2968 103095 10216 10199 2.0 8.0 NA
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## 2986 103113 10217 10197 9.0 10.0 10.0
## 2987 103114 10217 10198 7.0 9.0 8.0
## 2988 103115 10217 10199 7.0 NA NA
## 2989 103116 10217 10200 8.0 8.0 9.0
## 2990 103117 10217 10201 8.0 6.0 9.0
## 2991 103118 10217 10202 7.0 8.0 9.0
## 2992 103119 10217 10203 6.0 6.0 6.0
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## 2995 103122 10217 10206 8.0 9.0 9.0
## 2996 103123 10217 10207 6.0 5.0 NA
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## 2999 103126 10217 10210 8.0 8.0 9.0
## 3000 103127 10217 10211 8.0 8.0 8.0
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## 3046 103173 10220 10197 2.0 9.0 9.0
## 3047 103174 10220 10198 3.0 9.0 10.0
## 3048 103175 10220 10199 1.0 7.0 NA
## 3049 103176 10220 10200 2.0 9.0 10.0
## 3050 103177 10220 10201 3.0 9.0 10.0
## 3051 103178 10220 10202 1.0 6.0 8.0
## 3052 103179 10220 10203 3.0 10.0 10.0
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## 3058 103185 10220 10209 4.0 10.0 10.0
## 3059 103186 10220 10210 5.0 8.0 8.0
## 3060 103187 10220 10211 6.0 8.0 9.0
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## 3175 103302 10226 10206 5.0 8.0 8.0
## 3176 103303 10226 10207 5.0 6.0 7.0
## 3177 103304 10226 10208 5.0 8.0 NA
## 3178 103305 10226 10209 7.0 10.0 10.0
## 3179 103306 10226 10210 6.0 10.0 10.0
## 3180 103307 10226 10211 6.0 8.0 9.0
## 3181 103308 10227 10192 8.0 9.0 8.0
## 3182 103309 10227 10193 8.0 8.0 9.0
## 3183 103310 10227 10194 3.0 3.0 5.0
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## 3188 103315 10227 10199 7.0 7.0 8.0
## 3189 103316 10227 10200 9.0 9.0 9.0
## 3190 103317 10227 10201 8.0 3.0 9.0
## 3191 103318 10227 10202 8.0 7.0 8.0
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## 3194 103321 10227 10205 7.0 9.0 9.0
## 3195 103322 10227 10206 8.0 8.0 8.0
## 3196 103323 10227 10207 8.0 NA 8.0
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## 3198 103325 10227 10209 7.0 8.0 9.0
## 3199 103326 10227 10210 7.0 7.0 9.0
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## 3204 103331 10228 10195 7.0 7.0 7.0
## 3205 103332 10228 10196 5.0 5.0 5.0
## 3206 103333 10228 10197 2.0 4.0 4.0
## 3207 103334 10228 10198 8.0 7.0 NA
## 3208 103335 10228 10199 8.0 8.0 8.0
## 3209 103336 10228 10200 5.0 9.0 8.0
## 3210 103337 10228 10201 4.0 6.0 6.0
## 3211 103338 10228 10202 6.0 8.0 7.0
## 3212 103339 10228 10203 5.0 5.0 6.0
## 3213 103340 10228 10204 7.0 7.0 7.0
## 3214 103341 10228 10205 4.0 7.0 6.0
## 3215 103342 10228 10206 9.0 9.0 9.0
## 3216 103343 10228 10207 5.0 5.0 7.0
## 3217 103344 10228 10208 8.0 10.0 NA
## 3218 103345 10228 10209 6.0 9.0 8.0
## 3219 103346 10228 10210 6.0 6.0 6.0
## 3220 103347 10228 10211 5.0 8.0 8.0
## 3221 103348 10229 10192 8.0 10.0 9.0
## 3222 103349 10229 10193 6.0 10.0 9.0
## 3223 103350 10229 10194 3.0 6.0 7.0
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## 3227 103354 10229 10198 5.0 7.0 8.0
## 3228 103355 10229 10199 8.0 8.0 8.0
## 3229 103356 10229 10200 6.0 8.0 8.0
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## 3235 103362 10229 10206 5.0 9.0 9.0
## 3236 103363 10229 10207 8.0 8.0 8.0
## 3237 103364 10229 10208 5.0 2.0 3.0
## 3238 103365 10229 10209 7.0 8.0 9.0
## 3239 103366 10229 10210 7.0 7.0 7.0
## 3240 103367 10229 10211 6.0 10.0 10.0
## 3241 103368 10230 10192 5.0 10.0 9.0
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## 3246 103373 10230 10197 2.0 9.0 7.0
## 3247 103374 10230 10198 5.0 9.0 7.0
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## 3249 103376 10230 10200 7.0 9.0 9.0
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## 3252 103379 10230 10203 6.0 8.0 6.0
## 3253 103380 10230 10204 4.0 9.0 7.0
## 3254 103381 10230 10205 4.0 6.0 6.0
## 3255 103382 10230 10206 5.0 9.0 9.0
## 3256 103383 10230 10207 5.0 NA NA
## 3257 103384 10230 10208 3.0 9.0 9.0
## 3258 103385 10230 10209 5.0 10.0 10.0
## 3259 103386 10230 10210 6.0 6.0 7.0
## 3260 103387 10230 10211 6.0 7.0 7.0
## 3261 103388 10231 10192 6.0 7.0 9.0
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## 3263 103390 10231 10194 2.0 8.0 7.0
## 3264 103391 10231 10195 7.0 7.0 7.0
## 3265 103392 10231 10196 4.0 6.0 6.0
## 3266 103393 10231 10197 8.0 10.0 10.0
## 3267 103394 10231 10198 NA NA NA
## 3268 103395 10231 10199 4.0 8.0 8.0
## 3269 103396 10231 10200 7.0 8.0 8.0
## 3270 103397 10231 10201 6.0 10.0 9.0
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## 3272 103399 10231 10203 6.0 8.0 7.0
## 3273 103400 10231 10204 1.0 7.0 7.0
## 3274 103401 10231 10205 9.0 8.0 8.0
## 3275 103402 10231 10206 6.0 8.0 8.0
## 3276 103403 10231 10207 5.0 NA 7.0
## 3277 103404 10231 10208 4.0 2.0 2.0
## 3278 103405 10231 10209 5.0 5.0 7.0
## 3279 103406 10231 10210 6.0 7.0 7.0
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## 3281 103408 10232 10241 6.0 9.0 7.0
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## 3284 103411 10232 10244 3.0 6.0 7.0
## 3285 103412 10232 10245 3.0 7.0 5.0
## 3286 103413 10232 10246 5.0 7.0 8.0
## 3287 103414 10232 10247 9.0 9.0 8.0
## 3288 103415 10232 10248 5.0 10.0 6.0
## 3289 103416 10232 10249 8.0 8.0 8.0
## 3290 103417 10233 10241 8.0 8.0 8.0
## 3291 103418 10233 10242 9.0 9.0 7.0
## 3292 103419 10233 10243 7.0 6.0 6.0
## 3293 103420 10233 10244 6.0 3.0 5.0
## 3294 103421 10233 10245 8.0 7.0 7.0
## 3295 103422 10233 10246 7.0 8.0 6.0
## 3296 103423 10233 10247 9.0 8.0 9.0
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## 3299 103426 10234 10241 6.0 8.0 8.0
## 3300 103427 10234 10242 4.0 6.0 7.0
## 3301 103428 10234 10243 9.0 7.0 8.0
## 3302 103429 10234 10244 2.0 3.0 NA
## 3303 103430 10234 10245 7.0 7.0 5.0
## 3304 103431 10234 10246 6.0 6.0 9.0
## 3305 103432 10234 10247 9.0 9.0 9.0
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## 3307 103434 10234 10249 4.0 8.0 7.0
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## 3309 103436 10235 10242 2.0 6.0 8.0
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## 3311 103438 10235 10244 3.0 7.0 7.0
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## 3318 103445 10236 10242 8.0 8.0 8.0
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## 3344 103471 10239 10241 7.0 7.0 6.0
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## 3361 103488 10240 10249 8.0 9.0 8.0
## 3362 103489 10241 10232 4.0 8.0 9.0
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## 3395 103522 10244 10238 4.0 4.0 4.0
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## 3405 103532 10245 10239 5.0 10.0 10.0
## 3406 103533 10245 10240 1.0 7.0 6.0
## 3407 103534 10246 10232 5.0 6.0 6.0
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## 3422 103549 10247 10238 5.0 5.0 6.0
## 3423 103550 10247 10239 5.0 8.0 9.0
## 3424 103551 10247 10240 3.0 5.0 NA
## 3425 103552 10248 10232 7.0 5.0 8.0
## 3426 103553 10248 10233 3.0 7.0 7.0
## 3427 103554 10248 10234 7.0 6.0 7.0
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## 3446 103573 10250 10274 6.0 9.0 8.0
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## 3448 103575 10250 10276 7.0 7.0 8.0
## 3449 103576 10250 10277 6.5 8.0 9.0
## 3450 103577 10250 10278 7.0 8.0 7.0
## 3451 103578 10250 10279 10.0 8.0 8.0
## 3452 103579 10250 10280 7.0 7.0 7.0
## 3453 103580 10250 10281 9.0 9.0 9.0
## 3454 103581 10250 10282 7.0 7.0 NA
## 3455 103582 10250 10283 5.0 5.0 5.0
## 3456 103583 10250 10284 9.0 8.0 8.0
## 3457 103584 10250 10285 7.0 7.0 7.0
## 3458 103585 10250 10286 4.0 6.0 5.0
## 3459 103586 10250 10287 7.0 6.0 6.0
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## 3462 103589 10250 10290 6.0 10.0 6.0
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## 3469 103596 10251 10276 5.0 7.0 7.0
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## 3473 103600 10251 10280 6.0 6.0 9.0
## 3474 103601 10251 10281 3.0 3.0 3.0
## 3475 103602 10251 10282 2.0 5.0 NA
## 3476 103603 10251 10283 5.0 5.0 5.0
## 3477 103604 10251 10284 6.0 NA NA
## 3478 103605 10251 10285 4.0 7.0 6.0
## 3479 103606 10251 10286 3.0 6.0 6.0
## 3480 103607 10251 10287 2.0 3.0 6.0
## 3481 103608 10251 10288 2.0 5.0 6.0
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## 3494 103621 10252 10280 7.0 7.0 7.0
## 3495 103622 10252 10281 10.0 10.0 10.0
## 3496 103623 10252 10282 3.0 4.0 NA
## 3497 103624 10252 10283 8.0 8.0 8.0
## 3498 103625 10252 10284 7.0 NA 9.0
## 3499 103626 10252 10285 8.0 8.0 7.0
## 3500 103627 10252 10286 5.0 5.0 7.0
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## 3516 103643 10253 10281 9.0 9.0 9.0
## 3517 103644 10253 10282 7.0 7.0 NA
## 3518 103645 10253 10283 3.0 3.0 3.0
## 3519 103646 10253 10284 7.0 NA 7.0
## 3520 103647 10253 10285 7.0 7.0 5.0
## 3521 103648 10253 10286 5.0 7.0 3.0
## 3522 103649 10253 10287 7.0 4.0 7.0
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## 3537 103664 10254 10281 9.0 9.0 9.0
## 3538 103665 10254 10282 7.0 6.0 NA
## 3539 103666 10254 10283 6.0 6.0 6.0
## 3540 103667 10254 10284 6.0 NA 7.0
## 3541 103668 10254 10285 6.0 7.0 6.0
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## 3558 103685 10255 10281 10.0 10.0 10.0
## 3559 103686 10255 10282 7.0 7.0 NA
## 3560 103687 10255 10283 8.0 8.0 8.0
## 3561 103688 10255 10284 7.0 NA 8.0
## 3562 103689 10255 10285 8.0 6.0 7.0
## 3563 103690 10255 10286 4.0 5.0 6.0
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## 3572 103699 10256 10274 6.0 10.0 9.0
## 3573 103700 10256 10275 8.0 7.0 8.0
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## 3580 103707 10256 10282 7.0 7.0 7.0
## 3581 103708 10256 10283 7.0 7.0 7.0
## 3582 103709 10256 10284 8.0 NA 8.0
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## 325 7.0 5.0 5.0 6.0 8.0
## 326 7.0 7.0 6.0 9.0 8.0
## 327 7.0 7.0 5.0 6.0 7.0
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## 334 6.0 6.0 5.0 4.0 5.0
## 335 9.0 8.0 6.0 8.0 7.0
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## 341 6.0 6.0 4.0 3.0 7.0
## 342 7.0 8.0 6.0 7.0 7.0
## 343 7.0 8.0 6.0 3.0 5.0
## 344 10.0 10.0 10.0 7.0 7.0
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## 365 7.0 7.0 8.0 5.0 7.0
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## 367 7.0 10.0 7.0 8.0 9.0
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## 391 7.0 7.0 7.0 5.0 6.0
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## 396 6.0 7.0 2.0 4.0 4.0
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## 400 7.0 7.0 6.0 3.0 3.0
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## 406 8.0 8.0 7.0 5.0 4.0
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## 415 6.0 9.0 8.0 8.0 8.0
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## 422 8.0 8.0 6.0 8.0 8.0
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## 430 7.0 7.0 7.0 2.0 2.0
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## 454 7.0 8.0 7.0 7.0 7.0
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## 462 9.0 7.0 7.0 6.0 6.0
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## 476 7.0 5.0 3.0 7.0 9.0
## 477 5.0 5.0 4.0 4.0 7.0
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## 480 5.0 5.0 6.0 5.0 5.0
## 481 6.0 5.0 3.0 8.0 5.0
## 482 8.0 4.0 5.0 5.0 7.0
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## 485 6.0 7.0 5.0 3.0 9.0
## 486 7.0 9.0 6.0 8.0 7.0
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## 491 6.0 6.0 7.0 5.0 4.0
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## 494 3.0 5.0 2.0 8.0 9.0
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## 505 3.0 10.0 3.0 8.0 9.0
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## 507 5.0 9.0 5.0 5.0 5.0
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## 512 3.0 5.0 2.0 5.0 5.0
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## 519 4.0 4.0 NA 10.0 10.0
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## 521 1.0 8.0 2.0 5.0 5.0
## 522 3.0 4.0 3.0 5.0 5.0
## 523 1.0 10.0 1.0 5.0 5.0
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## 527 6.0 6.0 6.0 6.0 10.0
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## 530 3.0 7.0 3.0 7.0 8.0
## 531 2.0 6.0 1.0 8.0 9.0
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## 537 6.0 7.0 6.0 6.0 9.0
## 538 8.0 7.0 9.0 3.0 8.0
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## 540 7.0 5.0 4.0 4.0 8.0
## 541 7.0 7.0 7.0 6.0 10.0
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## 545 6.0 6.0 7.0 4.0 6.0
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## 548 1.0 7.0 2.0 6.0 5.0
## 549 6.0 4.0 3.0 7.0 6.0
## 550 3.0 5.0 NA 4.0 4.0
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## 552 5.0 5.0 5.0 6.0 6.0
## 553 6.0 9.0 5.0 5.0 6.0
## 554 5.0 7.0 5.0 4.0 4.0
## 555 4.0 6.0 NA 6.0 5.0
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## 557 1.0 1.0 1.0 6.0 5.0
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## 561 5.0 7.0 2.0 6.0 7.0
## 562 7.0 7.0 6.0 6.0 7.0
## 563 6.0 6.0 9.0 8.0 8.0
## 564 NA 8.0 6.0 5.0 7.0
## 565 7.0 9.0 3.0 7.0 6.0
## 566 6.0 6.0 4.0 8.0 7.0
## 567 2.0 2.0 1.0 9.0 7.0
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## 569 5.0 6.0 3.0 7.0 7.0
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## 571 5.0 4.0 6.0 7.0 6.0
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## 573 4.0 5.0 3.0 8.0 7.0
## 574 7.0 7.0 5.0 8.0 7.0
## 575 1.0 1.0 1.0 7.0 7.0
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## 578 4.0 5.0 1.0 8.0 5.0
## 579 6.0 8.0 4.0 7.0 8.0
## 580 4.0 6.0 6.0 7.0 7.0
## 581 6.0 7.0 5.0 7.0 8.0
## 582 NA 8.0 NA 6.0 6.0
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## 584 1.0 4.0 3.0 7.0 6.0
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## 587 2.0 6.0 5.0 6.0 6.0
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## 589 5.0 9.0 6.0 9.0 8.0
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## 592 7.0 8.0 9.0 8.0 6.0
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## 598 7.0 6.0 7.0 5.0 8.0
## 599 7.0 7.0 6.0 6.0 8.0
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## 602 6.0 5.0 4.0 10.0 9.0
## 603 6.0 4.0 4.0 9.0 8.0
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## 606 7.0 6.0 5.0 9.0 6.0
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## 608 5.0 6.0 4.0 6.0 8.0
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## 611 3.0 4.0 2.0 6.0 7.0
## 612 7.0 6.0 5.0 6.0 8.0
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## 617 6.0 6.0 6.0 5.0 6.0
## 618 7.0 7.0 7.0 6.0 6.0
## 619 8.0 10.0 5.0 6.0 7.0
## 620 4.0 6.0 2.0 5.0 6.0
## 621 4.0 4.0 4.0 7.0 5.0
## 622 7.0 6.0 NA 5.0 6.0
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## 624 2.0 6.0 2.0 7.0 6.0
## 625 7.0 8.0 8.0 5.0 7.0
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## 628 8.0 7.0 8.0 7.0 5.0
## 629 1.0 2.0 1.0 6.0 6.0
## 630 6.0 6.0 6.0 NA 3.0
## 631 7.0 4.0 3.0 6.0 7.0
## 632 8.0 5.0 7.0 3.0 7.0
## 633 5.0 6.0 3.0 3.0 8.0
## 634 7.0 7.0 6.0 4.0 6.0
## 635 8.0 6.0 5.0 4.0 6.0
## 636 8.0 7.0 NA 6.0 8.0
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## 638 7.0 6.0 3.0 9.0 8.0
## 639 5.0 6.0 7.0 8.0 8.0
## 640 8.0 8.0 6.0 6.0 8.0
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## 642 8.0 8.0 8.0 7.0 7.0
## 643 9.0 6.0 4.0 5.0 6.0
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## 645 3.0 2.0 2.0 7.0 7.0
## 646 8.0 7.0 6.0 7.0 7.0
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## 649 8.0 5.0 3.0 7.0 7.0
## 650 7.0 5.0 5.0 3.0 5.0
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## 652 5.0 7.0 4.0 7.0 8.0
## 653 7.0 6.0 7.0 7.0 8.0
## 654 8.0 NA 6.0 7.0 7.0
## 655 8.0 9.0 8.0 9.0 8.0
## 656 7.0 7.0 6.0 9.0 8.0
## 657 5.0 6.0 NA 8.0 8.0
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## 659 7.0 7.0 7.0 7.0 7.0
## 660 6.0 7.0 5.0 9.0 7.0
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## 663 7.0 8.0 6.0 8.0 8.0
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## 665 8.0 10.0 8.0 8.0 8.0
## 666 4.0 4.0 4.0 6.0 8.0
## 667 8.0 5.0 3.0 8.0 7.0
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## 670 7.0 7.0 7.0 6.0 8.0
## 671 8.0 8.0 7.0 5.0 NA
## 672 8.0 NA NA 7.0 NA
## 673 8.0 10.0 7.0 7.0 NA
## 674 7.0 6.0 6.0 8.0 NA
## 675 7.0 8.0 4.0 8.0 NA
## 676 7.0 7.0 NA 6.0 NA
## 677 8.0 7.0 7.0 6.0 NA
## 678 9.0 8.0 8.0 8.0 8.0
## 679 9.0 8.0 4.0 NA NA
## 680 7.0 10.0 5.0 6.0 8.0
## 681 6.0 7.0 4.0 7.0 7.0
## 682 10.0 8.0 9.0 7.0 NA
## 683 5.0 8.0 3.0 7.0 NA
## 684 8.0 4.0 4.0 5.0 NA
## 685 9.0 9.0 7.0 NA NA
## 687 4.0 4.0 2.0 2.0 8.0
## 688 4.0 4.0 2.0 2.0 2.0
## 689 4.0 4.0 4.0 4.0 6.0
## 690 7.0 8.0 NA 7.0 5.0
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## 692 5.0 6.0 6.0 7.0 3.0
## 693 1.0 6.0 2.0 8.0 4.0
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## 696 5.0 5.0 5.0 7.0 7.0
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## 700 6.0 7.0 6.0 8.0 4.0
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## 702 7.0 6.0 8.0 2.0 9.0
## 703 8.0 8.0 3.0 6.0 8.0
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## 712 5.0 7.0 5.0 5.0 7.0
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## 718 7.0 7.0 9.0 7.0 7.0
## 719 2.0 5.0 2.0 6.0 6.0
## 720 6.0 4.0 5.0 5.0 7.0
## 721 3.0 8.0 3.0 7.0 7.0
## 722 6.0 9.0 NA 4.0 6.0
## 723 7.0 7.0 2.0 6.0 8.0
## 724 6.0 7.0 8.0 6.0 8.0
## 725 NA NA NA 7.0 8.0
## 726 7.0 8.0 8.0 8.0 8.0
## 727 7.0 8.0 5.0 9.0 8.0
## 728 6.0 7.0 5.0 9.0 9.0
## 729 3.0 8.0 2.0 8.0 8.0
## 730 5.0 7.0 NA 6.0 8.0
## 731 7.0 9.0 7.0 6.0 8.0
## 732 6.0 6.0 6.0 8.0 8.0
## 733 8.0 9.0 9.0 8.0 9.0
## 734 6.0 7.0 4.0 7.0 9.0
## 735 3.0 7.0 3.0 8.0 8.0
## 736 8.0 8.0 6.0 8.0 7.0
## 737 5.0 6.0 7.0 7.0 8.0
## 738 4.0 7.0 4.0 7.0 8.0
## 739 3.0 9.0 5.0 6.0 9.0
## 740 5.0 5.0 NA 5.0 8.0
## 741 6.0 8.0 2.0 5.0 7.0
## 742 4.0 5.0 4.0 4.0 7.0
## 743 6.0 5.0 5.0 6.0 7.0
## 744 8.0 8.0 8.0 8.0 7.0
## 745 6.0 9.0 6.0 8.0 9.0
## 746 2.0 7.0 2.0 8.0 8.0
## 747 3.0 6.0 2.0 9.0 9.0
## 748 4.0 6.0 5.0 5.0 8.0
## 749 4.0 7.0 6.0 6.0 8.0
## 750 4.0 5.0 5.0 8.0 8.0
## 751 8.0 9.0 9.0 7.0 8.0
## 752 5.0 6.0 8.0 5.0 8.0
## 753 5.0 7.0 3.0 8.0 8.0
## 754 6.0 7.0 9.0 8.0 8.0
## 755 4.0 5.0 2.0 9.0 8.0
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## 1592 5.0 5.0 5.0 2.0 7.0
## 1593 10.0 10.0 10.0 8.0 10.0
## 1594 4.0 4.0 5.0 5.0 9.0
## 1595 5.0 5.0 8.0 5.0 9.0
## 1596 7.0 7.0 6.0 10.0 9.0
## 1597 9.0 8.0 6.0 6.0 9.0
## 1598 5.0 5.0 5.0 9.0 9.0
## 1599 8.0 8.0 8.0 6.0 10.0
## 1600 5.0 5.0 5.0 4.0 9.0
## 1601 7.0 7.0 7.0 7.0 6.0
## 1602 5.0 4.0 5.0 8.0 7.0
## 1603 7.0 7.0 7.0 4.0 3.0
## 1604 6.0 7.0 5.0 7.0 5.0
## 1605 6.0 7.0 7.0 5.0 6.0
## 1606 2.0 5.0 2.0 2.0 3.0
## 1607 8.0 8.0 7.0 8.0 6.0
## 1608 5.0 5.0 5.0 5.0 6.0
## 1609 10.0 10.0 10.0 10.0 10.0
## 1610 4.0 5.0 4.0 6.0 10.0
## 1611 4.0 4.0 6.0 2.0 10.0
## 1612 10.0 9.0 10.0 10.0 10.0
## 1613 6.0 6.0 5.0 6.0 10.0
## 1614 10.0 7.0 8.0 8.0 5.0
## 1615 10.0 8.0 7.0 9.0 10.0
## 1617 10.0 10.0 10.0 10.0 10.0
## 1618 4.0 4.0 6.0 7.0 10.0
## 1619 2.0 5.0 4.0 4.0 4.0
## 1620 10.0 10.0 10.0 10.0 8.0
## 1621 9.0 7.0 7.0 6.0 9.0
## 1622 7.0 5.0 5.0 8.0 6.0
## 1623 7.0 8.0 6.0 7.0 8.0
## 1624 5.0 5.0 5.0 4.0 8.0
## 1625 10.0 10.0 10.0 10.0 10.0
## 1626 6.0 5.0 6.0 7.0 10.0
## 1627 5.0 5.0 5.0 4.0 8.0
## 1628 10.0 10.0 10.0 10.0 10.0
## 1629 7.0 7.0 8.0 6.0 10.0
## 1630 8.0 10.0 6.0 8.0 6.0
## 1631 9.0 7.0 7.0 7.0 8.0
## 1633 7.0 8.0 7.0 3.0 8.0
## 1634 6.0 7.0 7.0 NA 6.0
## 1635 5.0 3.0 2.0 4.0 9.0
## 1636 6.0 6.0 7.0 6.0 8.0
## 1637 9.0 7.0 7.0 5.0 4.0
## 1638 6.0 7.0 3.0 8.0 10.0
## 1639 7.0 8.0 8.0 5.0 7.0
## 1640 6.0 6.0 6.0 2.0 7.0
## 1641 7.0 7.0 7.0 7.0 8.0
## 1642 5.0 7.0 4.0 5.0 8.0
## 1643 6.0 8.0 7.0 2.0 1.0
## 1644 8.0 7.0 7.0 6.0 8.0
## 1645 7.0 6.0 8.0 8.0 10.0
## 1646 8.0 5.0 6.0 9.0 4.0
## 1647 10.0 8.0 7.0 8.0 7.0
## 1648 6.0 6.0 2.0 7.0 8.0
## 1649 7.0 6.0 6.0 8.0 10.0
## 1650 4.0 5.0 4.0 9.0 10.0
## 1651 6.0 7.0 7.0 4.0 6.0
## 1652 5.0 4.0 2.0 6.0 10.0
## 1653 6.0 6.0 5.0 8.0 7.0
## 1654 0.0 5.0 0.0 4.0 4.0
## 1655 7.0 8.0 7.0 9.0 10.0
## 1656 6.0 7.0 2.0 7.0 10.0
## 1657 9.0 7.0 8.0 9.0 9.0
## 1658 10.0 5.0 5.0 10.0 10.0
## 1659 7.0 4.0 4.0 6.0 10.0
## 1660 10.0 10.0 10.0 10.0 10.0
## 1661 10.0 10.0 10.0 10.0 10.0
## 1662 10.0 10.0 10.0 10.0 10.0
## 1663 5.0 5.0 NA 9.0 9.0
## 1664 8.0 8.0 5.0 6.0 7.0
## 1665 7.0 7.0 8.0 6.0 9.0
## 1666 4.0 9.0 3.0 9.0 7.0
## 1667 5.0 5.0 2.0 6.0 5.0
## 1668 6.0 5.0 4.0 4.0 6.0
## 1669 7.0 6.0 5.0 4.0 5.0
## 1670 8.0 8.0 6.0 6.0 5.0
## 1671 7.0 8.0 4.0 6.0 6.0
## 1672 8.0 9.0 4.0 6.0 4.0
## 1673 2.0 2.0 2.0 9.0 6.0
## 1674 5.0 10.0 9.0 5.0 4.0
## 1675 2.0 8.0 1.0 6.0 7.0
## 1676 1.0 10.0 1.0 7.0 6.0
## 1677 2.0 9.0 3.0 5.0 6.0
## 1678 2.0 10.0 2.0 6.0 2.0
## 1679 2.0 10.0 0.0 5.0 4.0
## 1680 5.0 9.0 3.0 8.0 7.0
## 1681 NA 8.0 NA 9.0 8.0
## 1682 1.0 6.0 0.0 7.0 5.0
## 1683 6.0 6.0 1.0 2.0 7.0
## 1684 8.0 7.0 8.0 9.0 8.0
## 1685 10.0 10.0 7.0 8.0 8.0
## 1686 6.0 5.0 3.0 5.0 8.0
## 1687 10.0 10.0 10.0 10.0 10.0
## 1688 9.0 8.0 8.0 10.0 10.0
## 1689 10.0 10.0 10.0 10.0 5.0
## 1690 9.0 8.0 6.0 8.0 8.0
## 1691 6.0 8.0 5.0 9.0 8.0
## 1692 8.0 8.0 4.0 4.0 6.0
## 1693 6.0 8.0 7.0 7.0 8.0
## 1694 7.0 7.0 6.0 6.0 8.0
## 1695 5.0 5.0 7.0 6.0 7.0
## 1696 8.0 10.0 6.0 5.0 7.0
## 1697 7.0 9.0 4.0 7.0 6.0
## 1698 7.0 8.0 6.0 7.0 7.0
## 1699 7.0 6.0 NA 6.0 9.0
## 1700 7.0 8.0 6.0 7.0 7.0
## 1701 7.0 10.0 9.0 4.0 8.0
## 1702 8.0 8.0 8.0 10.0 7.0
## 1703 10.0 7.0 8.0 5.0 5.0
## 1704 2.0 6.0 2.0 5.0 7.0
## 1705 9.0 10.0 8.0 7.0 3.0
## 1706 7.0 5.0 6.0 7.0 7.0
## 1707 6.0 8.0 5.0 7.0 7.0
## 1708 10.0 8.0 7.0 9.0 8.0
## 1709 2.0 2.0 1.0 8.0 5.0
## 1710 1.0 1.0 1.0 5.0 8.0
## 1711 8.0 7.0 9.0 10.0 9.0
## 1712 8.0 7.0 4.0 7.0 9.0
## 1713 8.0 7.0 5.0 7.0 9.0
## 1714 8.0 8.0 7.0 9.0 7.0
## 1715 6.0 6.0 3.0 7.0 7.0
## 1716 6.0 6.0 6.0 8.0 8.0
## 1717 NA NA NA 9.0 8.0
## 1718 6.0 5.0 2.0 8.0 8.0
## 1719 5.0 7.0 4.0 6.0 9.0
## 1720 2.0 5.0 1.0 5.0 5.0
## 1721 3.0 6.0 2.0 5.0 5.0
## 1722 5.0 3.0 3.0 5.0 5.0
## 1724 3.0 7.0 0.0 5.0 5.0
## 1726 2.0 NA 1.0 6.0 6.0
## 1727 5.0 7.0 2.0 6.0 6.0
## 1728 8.0 10.0 1.0 7.0 5.0
## 1729 8.0 9.0 7.0 7.0 9.0
## 1730 6.0 6.0 4.0 8.0 8.0
## 1731 8.0 7.0 NA 5.0 7.0
## 1732 7.0 7.0 7.0 8.0 9.0
## 1733 7.0 7.0 8.0 5.0 8.0
## 1734 6.0 7.0 5.0 7.0 7.0
## 1735 9.0 8.0 1.0 7.0 6.0
## 1736 6.0 8.0 NA 6.0 5.0
## 1737 6.0 8.0 6.0 8.0 7.0
## 1738 9.0 6.0 5.0 5.0 6.0
## 1739 8.0 8.0 7.0 8.0 9.0
## 1740 2.0 7.0 1.0 7.0 8.0
## 1741 7.0 6.0 NA 7.0 7.0
## 1742 7.0 7.0 NA 8.0 9.0
## 1743 7.0 6.0 5.0 6.0 5.0
## 1744 7.0 7.0 6.0 8.0 5.0
## 1745 1.0 7.0 1.0 8.0 8.0
## 1746 6.0 5.0 NA 7.0 8.0
## 1747 7.0 7.0 7.0 8.0 8.0
## 1748 8.0 6.0 5.0 7.0 NA
## 1749 9.0 6.0 9.0 8.0 8.0
## 1750 8.0 6.0 8.0 6.0 10.0
## 1751 9.0 10.0 8.0 9.0 9.0
## 1752 NA NA 5.0 7.0 8.0
## 1753 8.0 8.0 8.0 8.0 7.0
## 1754 7.0 8.0 6.0 3.0 8.0
## 1755 8.0 7.0 8.0 3.0 8.0
## 1756 NA NA 2.0 4.0 6.0
## 1757 5.0 9.0 5.0 8.0 NA
## 1758 6.0 6.0 7.0 6.0 8.0
## 1759 5.0 8.0 5.0 7.0 9.0
## 1760 8.0 4.0 6.0 6.0 NA
## 1761 9.0 9.0 8.0 7.0 7.0
## 1762 8.0 8.0 7.0 5.0 9.0
## 1763 9.0 10.0 7.0 6.5 8.0
## 1764 8.0 NA NA 6.5 8.0
## 1765 8.0 7.0 7.0 8.0 7.0
## 1766 7.0 5.0 6.0 6.0 10.0
## 1767 5.0 6.0 4.0 7.0 8.0
## 1768 8.0 8.0 NA 8.0 9.0
## 1769 7.0 7.0 5.0 4.0 5.0
## 1770 6.0 NA 4.0 5.0 10.0
## 1771 7.0 9.0 NA 8.0 8.0
## 1772 7.0 6.0 4.0 10.0 10.0
## 1773 6.0 6.0 5.0 8.0 10.0
## 1774 5.0 6.0 5.0 6.0 8.0
## 1775 7.0 NA NA 8.0 9.0
## 1776 7.0 8.0 6.0 6.0 8.0
## 1777 4.0 NA NA 8.0 6.0
## 1778 6.0 4.0 4.0 8.0 8.0
## 1779 7.0 7.0 8.0 6.0 9.0
## 1780 8.0 7.0 8.0 10.0 10.0
## 1781 7.0 5.0 7.0 4.0 7.0
## 1782 9.0 8.0 7.0 4.0 10.0
## 1783 7.0 7.0 6.0 8.0 7.0
## 1784 7.0 5.0 5.0 6.0 4.0
## 1785 6.0 8.0 7.0 5.0 6.0
## 1786 5.0 4.0 4.0 4.0 6.0
## 1787 5.0 8.0 3.0 5.0 5.0
## 1788 5.0 10.0 6.0 5.0 6.0
## 1789 2.0 7.0 2.0 6.0 7.0
## 1790 6.0 6.0 4.0 9.0 8.0
## 1791 5.0 NA NA 8.0 7.0
## 1792 6.0 8.0 4.0 10.0 9.0
## 1793 5.0 NA NA 9.0 8.0
## 1794 4.0 5.0 2.0 5.0 6.0
## 1795 6.0 6.0 6.0 7.0 8.0
## 1796 8.0 8.0 8.0 8.0 9.0
## 1797 5.0 7.0 3.0 5.0 5.0
## 1798 6.0 7.0 9.0 8.0 9.0
## 1799 2.0 8.0 1.0 8.0 8.0
## 1800 4.0 5.0 5.0 7.0 5.0
## 1801 7.0 7.0 8.0 4.0 6.0
## 1802 8.0 7.0 9.0 4.0 7.0
## 1803 9.0 9.0 7.0 7.0 8.0
## 1804 9.0 3.0 1.0 8.0 8.0
## 1805 7.0 7.0 5.0 7.0 7.0
## 1806 6.0 6.0 5.0 6.0 7.0
## 1807 7.0 6.0 5.0 8.0 8.0
## 1808 8.0 7.0 6.0 7.0 6.0
## 1809 9.0 NA NA 7.0 8.0
## 1810 4.0 5.0 6.0 6.0 7.0
## 1811 8.0 8.0 8.0 5.0 5.0
## 1812 9.0 7.0 9.0 7.0 7.0
## 1813 NA NA NA 5.0 7.0
## 1814 9.0 8.0 10.0 5.0 7.0
## 1815 5.0 6.0 4.0 5.0 8.0
## 1816 6.0 6.0 5.0 6.0 7.0
## 1817 7.0 7.0 8.0 7.0 10.0
## 1818 7.0 7.0 9.0 7.0 10.0
## 1819 4.0 10.0 3.0 8.0 10.0
## 1820 7.0 5.0 3.0 10.0 9.0
## 1821 7.0 6.0 5.0 9.0 9.0
## 1822 8.0 5.0 3.0 9.0 9.0
## 1823 8.0 NA 7.0 9.0 10.0
## 1824 6.0 8.0 5.0 8.0 8.0
## 1825 6.0 NA NA 7.0 7.0
## 1826 4.0 5.0 5.0 10.0 9.0
## 1827 7.0 7.0 7.0 6.0 9.0
## 1828 8.0 9.0 8.0 9.0 7.0
## 1829 6.0 6.0 8.0 8.0 10.0
## 1830 5.0 8.0 3.0 9.0 9.0
## 1831 2.0 4.0 2.0 9.0 9.0
## 1832 5.0 5.0 5.0 8.0 10.0
## 1833 7.0 7.0 8.0 4.0 6.0
## 1834 5.0 6.0 NA 5.0 7.0
## 1835 6.0 7.0 NA 8.0 10.0
## 1836 4.0 5.0 5.0 6.0 8.0
## 1837 3.0 6.0 3.0 5.0 5.0
## 1838 6.0 7.0 7.0 6.0 8.0
## 1839 8.0 8.0 8.0 10.0 9.0
## 1840 7.0 7.0 7.0 8.0 8.0
## 1841 7.0 NA NA 5.0 8.0
## 1842 2.0 5.0 3.0 5.0 7.0
## 1843 6.0 6.0 3.0 6.0 8.0
## 1844 7.0 8.0 7.0 8.0 8.0
## 1845 6.0 6.0 5.0 6.0 8.0
## 1846 6.0 7.0 7.0 8.0 8.0
## 1847 1.0 3.0 1.0 8.0 8.0
## 1848 5.0 5.0 4.0 6.0 8.0
## 1849 9.0 8.0 6.0 5.0 5.0
## 1850 6.0 6.0 3.0 5.0 6.0
## 1851 8.0 8.0 7.0 8.0 6.0
## 1852 10.0 6.0 6.0 7.0 6.0
## 1853 8.0 7.0 5.0 5.0 6.0
## 1854 8.0 7.0 8.0 7.0 8.0
## 1855 8.0 6.0 5.0 6.0 8.0
## 1856 10.0 8.0 5.0 7.0 7.0
## 1857 9.0 NA NA 8.0 8.0
## 1858 8.0 2.0 4.0 6.0 6.0
## 1859 7.0 5.0 7.0 4.0 8.0
## 1860 9.0 8.0 NA 8.0 7.0
## 1861 6.0 6.0 7.0 4.0 7.0
## 1862 9.0 7.0 4.0 4.0 7.0
## 1863 7.0 8.0 6.0 8.0 10.0
## 1864 6.0 6.0 4.0 6.0 8.0
## 1865 7.0 7.0 7.0 4.0 8.0
## 1866 5.0 5.0 NA 3.0 8.0
## 1867 8.0 8.0 7.0 7.0 5.0
## 1868 5.0 7.0 5.0 5.0 8.0
## 1869 7.0 6.0 7.0 6.0 8.0
## 1870 7.0 6.0 5.0 7.0 9.0
## 1871 NA NA NA 4.0 6.0
## 1872 6.0 9.0 5.0 6.0 7.0
## 1873 8.0 8.0 NA 7.0 8.0
## 1874 5.0 4.0 3.0 3.0 8.0
## 1875 6.0 6.0 5.0 2.0 8.0
## 1876 NA 9.0 NA 8.0 8.0
## 1877 5.0 8.0 5.0 3.0 8.0
## 1878 6.0 8.0 6.0 4.0 8.0
## 1879 9.0 8.0 4.0 7.0 8.0
## 1880 7.0 6.0 5.0 7.0 9.0
## 1881 8.0 7.0 6.0 6.0 7.0
## 1882 7.0 7.0 6.0 4.0 7.0
## 1883 6.0 8.0 NA 9.0 7.0
## 1884 5.0 8.0 7.0 8.0 6.0
## 1885 7.0 7.0 5.0 4.0 8.0
## 1886 7.0 7.0 6.0 5.0 6.0
## 1887 6.0 NA NA 3.0 7.0
## 1888 7.0 6.0 5.0 8.0 7.0
## 1889 7.0 NA NA 8.0 5.0
## 1890 5.0 4.0 4.0 4.0 6.0
## 1891 6.0 6.0 6.0 4.0 7.0
## 1892 8.0 8.0 8.0 8.0 7.0
## 1893 6.0 6.0 6.0 4.0 8.0
## 1894 6.0 9.0 10.0 6.0 8.0
## 1895 5.0 6.0 7.0 7.0 7.0
## 1896 8.0 7.0 8.0 5.0 8.0
## 1897 7.0 7.0 7.0 5.0 5.0
## 1898 8.0 8.0 5.0 5.0 8.0
## 1899 8.0 8.0 3.0 9.0 7.0
## 1900 8.0 6.0 5.0 8.0 5.0
## 1901 5.0 6.0 3.0 6.0 6.0
## 1902 8.0 8.0 5.0 7.0 7.0
## 1903 9.0 NA 9.0 8.0 7.0
## 1904 8.0 8.0 7.0 9.0 8.0
## 1905 8.0 9.0 NA 6.0 5.0
## 1906 7.0 5.0 4.0 7.0 7.0
## 1907 7.0 7.0 7.0 7.0 8.0
## 1908 7.0 9.0 8.0 9.0 9.0
## 1909 7.0 6.0 7.0 5.0 6.0
## 1910 7.0 8.0 5.0 6.0 7.0
## 1911 4.0 6.0 3.0 6.0 7.0
## 1912 7.0 7.0 5.0 6.0 8.0
## 1913 9.0 7.0 6.0 4.0 5.0
## 1914 7.0 7.0 NA 3.0 7.0
## 1915 8.0 7.0 8.0 8.0 6.0
## 1916 9.0 8.0 5.0 9.0 7.0
## 1917 7.0 6.0 9.0 9.0 8.0
## 1918 7.0 6.0 5.0 7.0 5.0
## 1919 9.0 NA NA 6.0 8.0
## 1920 10.0 7.0 3.0 8.0 5.0
## 1921 8.0 NA NA 6.0 4.0
## 1922 4.0 3.0 4.0 6.0 3.0
## 1923 7.0 8.0 7.0 6.0 3.0
## 1924 9.0 8.0 7.0 7.0 8.0
## 1925 8.0 7.0 5.0 3.0 8.0
## 1926 8.0 7.0 6.0 5.0 7.0
## 1927 6.0 8.0 6.0 7.0 8.0
## 1928 8.0 7.0 7.0 8.0 7.0
## 1929 7.0 7.0 6.0 6.0 7.0
## 1930 5.0 NA 3.0 4.0 6.0
## 1931 6.0 9.0 3.0 6.0 8.0
## 1932 6.0 5.0 2.0 8.0 8.0
## 1933 5.0 6.0 4.0 5.0 6.0
## 1934 6.0 7.0 5.0 7.0 8.0
## 1935 NA NA NA 6.0 8.0
## 1936 5.0 9.0 5.0 8.0 8.0
## 1937 5.0 NA NA 5.0 5.0
## 1938 6.0 5.0 3.0 7.0 9.0
## 1939 7.0 7.0 6.0 4.0 7.0
## 1940 7.0 8.0 6.0 8.0 9.0
## 1941 5.0 6.0 5.0 4.0 6.0
## 1942 5.0 7.0 4.0 6.0 7.0
## 1943 4.0 6.0 3.0 6.0 8.0
## 1944 5.0 5.0 5.0 5.0 8.0
## 1945 6.0 7.0 6.0 6.0 6.0
## 1946 6.0 6.0 5.0 5.0 5.0
## 1947 6.0 7.0 2.0 5.0 6.0
## 1948 6.0 5.0 3.0 7.0 7.0
## 1949 3.0 7.0 3.0 5.0 5.0
## 1950 8.0 8.0 6.0 8.0 8.0
## 1951 6.0 NA 6.0 6.0 6.0
## 1952 6.0 7.0 5.0 5.0 5.0
## 1953 4.0 NA NA 4.0 4.0
## 1954 5.0 6.0 4.0 5.0 6.0
## 1955 6.0 6.0 5.0 5.0 6.0
## 1956 7.0 8.0 6.0 7.0 7.0
## 1957 6.0 5.0 7.0 5.0 5.0
## 1958 6.0 7.0 4.0 6.0 6.0
## 1959 2.0 NA 1.0 6.0 5.0
## 1960 5.0 5.0 5.0 5.0 5.0
## 1961 8.0 7.0 6.0 5.0 8.0
## 1962 7.0 8.0 6.0 6.0 8.0
## 1963 7.0 8.0 7.0 9.0 9.0
## 1964 9.0 7.0 6.0 10.0 8.0
## 1965 6.0 7.0 3.0 5.0 8.0
## 1966 8.0 7.0 5.0 9.0 9.0
## 1967 NA NA NA 7.0 9.0
## 1968 8.0 10.0 3.0 7.0 9.0
## 1969 7.0 NA NA 9.0 9.0
## 1970 6.0 6.0 6.0 8.0 8.0
## 1971 8.0 8.0 8.0 5.0 9.0
## 1972 9.0 9.0 10.0 9.0 9.0
## 1973 5.0 7.0 7.0 6.0 10.0
## 1974 8.0 10.0 5.0 8.0 9.0
## 1975 5.0 6.0 6.0 9.0 10.0
## 1976 7.0 6.0 6.0 9.0 9.0
## 1977 8.0 7.0 7.0 2.0 6.0
## 1978 7.0 5.0 NA 1.0 4.0
## 1979 8.0 NA NA 7.0 7.0
## 1980 10.0 7.0 6.0 8.0 7.0
## 1981 4.0 7.0 3.0 2.0 4.0
## 1982 6.0 6.0 7.0 6.0 7.0
## 1983 NA 7.0 5.0 6.0 8.0
## 1984 6.0 5.0 5.0 7.0 6.0
## 1985 7.0 NA NA 8.0 7.0
## 1986 4.0 4.0 3.0 5.0 5.0
## 1987 7.0 7.0 7.0 3.0 6.0
## 1988 8.0 8.0 8.0 8.0 8.0
## 1989 6.0 6.0 6.0 2.0 8.0
## 1990 8.0 9.0 6.0 4.0 7.0
## 1991 4.0 5.0 5.0 5.0 8.0
## 1992 8.0 7.0 5.0 3.0 8.0
## 1993 8.0 7.0 6.0 4.0 7.0
## 1994 5.0 5.0 NA 3.0 10.0
## 1995 9.0 9.0 10.0 7.0 10.0
## 1996 8.0 8.0 4.0 8.0 9.0
## 1997 5.0 9.0 6.0 7.0 6.0
## 1998 8.0 8.0 8.0 6.0 9.0
## 1999 7.0 9.0 5.0 7.0 9.0
## 2000 7.0 8.0 6.0 8.0 5.0
## 2001 7.0 NA NA 7.0 6.0
## 2002 5.0 6.0 4.0 6.0 9.0
## 2003 7.0 7.0 6.0 6.0 9.0
## 2004 8.0 10.0 8.0 7.0 9.0
## 2005 7.0 8.0 8.0 4.0 8.0
## 2006 9.0 10.0 4.0 5.0 8.0
## 2007 7.0 8.0 7.0 5.0 9.0
## 2008 7.0 7.0 6.0 6.0 9.0
## 2009 7.0 6.0 5.0 2.0 3.0
## 2010 6.0 NA NA 2.0 6.0
## 2011 9.0 9.0 8.0 7.0 6.0
## 2012 9.0 7.0 6.0 7.0 7.0
## 2013 6.0 7.0 3.0 3.0 5.0
## 2014 6.0 7.0 4.0 5.0 7.0
## 2015 9.0 NA NA 5.0 6.0
## 2016 6.0 8.0 5.0 6.0 7.0
## 2017 8.0 NA NA 6.0 7.0
## 2018 6.0 5.0 4.0 4.0 5.0
## 2019 8.0 8.0 8.0 2.0 8.0
## 2020 9.0 9.0 8.0 8.0 7.0
## 2021 5.0 7.0 6.0 3.0 6.0
## 2022 9.0 10.0 5.0 5.0 7.0
## 2023 8.0 9.0 6.0 7.0 7.0
## 2024 8.0 8.0 7.0 5.0 6.0
## 2025 4.0 6.0 4.0 10.0 8.0
## 2026 6.0 7.0 NA 6.0 8.0
## 2027 4.0 7.0 4.0 9.0 9.0
## 2028 7.0 9.0 8.0 9.0 8.0
## 2029 NA NA NA 8.0 9.0
## 2030 5.0 5.0 5.0 9.0 7.0
## 2031 2.0 6.0 1.0 9.0 8.0
## 2032 4.0 7.0 NA 9.0 9.0
## 2033 6.0 9.0 NA 10.0 8.0
## 2034 1.0 3.0 1.0 10.0 8.0
## 2035 6.0 8.0 6.0 9.0 9.0
## 2036 6.0 NA NA 7.0 9.0
## 2037 5.0 8.0 7.0 9.0 9.0
## 2038 2.0 7.0 2.0 10.0 9.0
## 2039 3.0 10.0 3.0 9.0 8.0
## 2040 2.0 3.0 1.0 9.0 8.0
## 2041 8.0 7.0 5.0 7.0 7.0
## 2042 5.0 NA 5.0 5.0 7.0
## 2043 5.0 7.0 4.0 8.0 8.0
## 2044 10.0 10.0 8.0 8.0 8.0
## 2045 8.0 NA NA 6.0 8.0
## 2046 5.0 5.0 4.0 6.0 6.0
## 2047 2.0 7.0 1.0 7.0 6.0
## 2048 4.0 6.0 7.0 8.0 9.0
## 2049 8.0 8.0 NA 8.0 9.0
## 2050 4.0 6.0 5.0 8.0 8.0
## 2051 4.0 NA NA 7.0 7.0
## 2052 8.0 NA 5.0 7.0 7.0
## 2053 8.0 9.0 5.0 7.0 8.0
## 2054 3.0 6.0 2.0 7.0 9.0
## 2055 6.0 10.0 3.0 6.0 7.0
## 2056 6.0 5.0 4.0 6.0 10.0
## 2057 8.0 10.0 6.0 6.0 9.0
## 2058 8.0 7.0 4.0 4.0 9.0
## 2059 8.0 8.0 7.0 6.0 8.0
## 2060 10.0 7.0 5.0 4.0 8.0
## 2061 8.0 8.0 NA 5.0 8.0
## 2062 8.0 9.0 7.0 8.0 10.0
## 2063 8.0 8.0 6.0 7.0 8.0
## 2064 8.0 8.0 6.0 6.0 9.0
## 2065 7.0 7.0 2.0 7.0 10.0
## 2066 7.0 7.0 6.0 8.0 8.0
## 2067 7.0 7.0 4.0 7.0 9.0
## 2068 6.0 6.0 NA 7.0 9.0
## 2069 9.0 9.0 9.0 8.0 8.0
## 2070 7.0 6.0 6.0 7.0 8.0
## 2071 9.0 10.0 10.0 8.0 10.0
## 2072 10.0 9.0 8.0 10.0 8.0
## 2073 8.0 8.0 7.0 8.0 7.0
## 2074 9.0 8.0 6.0 5.0 10.0
## 2075 10.0 7.0 9.0 6.0 8.0
## 2076 9.0 8.0 7.0 6.0 8.0
## 2077 NA NA NA 5.0 6.0
## 2078 6.0 6.0 5.0 7.0 8.0
## 2079 5.0 5.0 1.0 6.0 6.0
## 2080 8.0 6.0 8.0 7.0 6.0
## 2081 8.0 8.0 7.0 8.0 8.0
## 2082 8.0 7.0 7.0 10.0 8.0
## 2083 8.0 9.0 9.0 7.0 10.0
## 2084 7.0 NA 6.0 7.0 9.0
## 2085 9.0 8.0 6.0 9.0 9.0
## 2086 8.0 7.0 5.0 7.0 8.0
## 2087 7.0 9.0 5.0 7.0 9.0
## 2088 7.0 6.0 7.0 8.0 8.0
## 2089 8.0 8.0 5.0 6.0 7.0
## 2090 8.0 9.0 NA 3.0 6.0
## 2091 8.0 7.0 7.0 8.0 7.0
## 2092 8.0 9.0 8.0 5.0 6.0
## 2093 NA 8.0 NA 4.0 6.0
## 2094 6.0 5.0 4.0 5.0 7.0
## 2095 4.0 9.0 7.0 6.0 6.0
## 2096 4.0 8.0 NA 8.0 6.0
## 2097 6.0 6.0 5.0 6.0 9.0
## 2098 7.0 6.0 9.0 8.0 7.0
## 2099 7.0 NA 8.0 6.0 7.0
## 2100 6.0 7.0 6.0 5.0 7.0
## 2101 7.0 7.0 2.0 7.0 6.0
## 2102 4.0 6.0 3.0 6.0 5.0
## 2103 3.0 8.0 10.0 5.0 7.0
## 2104 2.0 5.0 2.0 5.0 5.0
## 2105 8.0 8.0 7.0 7.0 7.0
## 2106 8.0 9.0 8.0 5.0 8.0
## 2107 8.0 7.0 7.0 8.0 8.0
## 2108 7.0 9.0 7.0 6.0 7.0
## 2109 8.0 NA NA 5.0 8.0
## 2110 7.0 7.0 8.0 7.0 9.0
## 2111 8.0 8.0 7.0 8.0 8.0
## 2112 4.0 4.0 4.0 8.0 8.0
## 2113 8.0 6.0 NA 9.0 9.0
## 2114 8.0 7.0 7.0 8.0 9.0
## 2115 7.0 6.0 8.0 6.0 8.0
## 2116 8.0 6.0 7.0 8.0 10.0
## 2117 9.0 8.0 6.0 8.0 10.0
## 2118 6.0 5.0 7.0 8.0 6.0
## 2119 7.0 10.0 8.0 8.0 9.0
## 2120 5.0 5.0 4.0 7.0 6.0
## 2121 8.0 5.0 6.0 8.0 7.0
## 2122 8.0 8.0 NA 4.0 9.0
## 2123 7.0 6.0 7.0 7.0 7.0
## 2124 9.0 8.0 9.0 4.0 8.0
## 2125 10.0 8.0 8.0 9.0 10.0
## 2126 6.0 6.0 5.0 9.0 6.0
## 2127 6.0 7.0 2.0 7.0 7.0
## 2128 3.0 3.0 3.0 6.0 6.0
## 2129 7.0 6.0 7.0 9.0 10.0
## 2130 5.0 7.0 8.0 9.0 9.0
## 2131 7.0 NA 9.0 8.0 9.0
## 2132 7.0 NA 6.0 7.0 8.0
## 2133 9.0 7.0 5.0 8.0 7.0
## 2134 7.0 8.0 8.0 5.0 10.0
## 2135 6.0 10.0 6.0 8.0 8.0
## 2136 7.0 7.0 6.0 8.0 7.0
## 2137 6.0 5.0 7.0 8.0 8.0
## 2138 10.0 6.0 NA 6.0 9.0
## 2139 5.0 6.0 5.0 7.0 8.0
## 2140 7.0 5.0 4.0 6.0 8.0
## 2141 NA NA 7.0 8.0 7.0
## 2142 5.0 6.0 5.0 7.0 8.0
## 2143 1.0 5.0 2.0 8.0 8.0
## 2144 4.0 4.0 NA 9.0 7.0
## 2145 7.0 8.0 NA 8.0 10.0
## 2146 4.0 6.0 5.0 10.0 9.0
## 2147 8.0 8.0 9.0 8.0 9.0
## 2148 5.0 NA 6.0 7.0 8.0
## 2149 5.0 5.0 4.0 10.0 8.0
## 2150 6.0 4.0 5.0 9.0 9.0
## 2151 7.0 8.0 6.0 7.0 8.0
## 2152 4.0 5.0 4.0 9.0 7.0
## 2153 8.0 9.0 5.0 4.0 4.0
## 2154 8.0 8.0 NA 3.0 7.0
## 2155 7.0 7.0 6.0 7.0 7.0
## 2156 7.0 7.0 4.0 5.0 7.0
## 2157 6.0 NA NA 7.0 7.0
## 2158 6.0 8.0 7.0 8.0 8.0
## 2159 5.0 5.0 2.0 8.0 8.0
## 2160 3.0 7.0 8.0 7.0 7.0
## 2161 5.0 8.0 NA 5.0 7.0
## 2162 6.0 5.0 6.0 8.0 8.0
## 2163 5.0 NA 7.0 8.0 8.0
## 2164 4.0 NA NA 6.0 7.0
## 2165 9.0 9.0 7.0 7.0 7.0
## 2166 6.0 6.0 5.0 7.0 7.0
## 2167 9.0 10.0 6.0 7.0 7.0
## 2168 7.0 7.0 6.0 8.0 8.0
## 2169 8.0 8.0 8.0 7.0 6.0
## 2170 6.0 4.0 NA 3.0 7.0
## 2171 7.0 7.0 7.0 4.0 6.0
## 2172 8.0 7.0 4.0 2.0 6.0
## 2173 7.0 NA NA 3.0 6.0
## 2174 6.0 6.0 5.0 3.0 6.0
## 2175 5.0 8.0 2.0 6.0 5.0
## 2176 7.0 5.0 8.0 6.0 6.0
## 2177 8.0 8.0 8.0 7.0 5.0
## 2178 5.0 4.0 7.0 8.0 8.0
## 2179 8.0 8.0 7.0 7.0 8.0
## 2180 7.0 NA 5.0 4.0 8.0
## 2181 8.0 8.0 8.0 5.0 7.0
## 2182 6.0 6.0 3.0 6.0 6.0
## 2183 6.0 9.0 4.0 7.0 6.0
## 2184 6.0 3.0 4.0 6.0 7.0
## 2185 10.0 9.0 10.0 8.0 8.0
## 2186 9.0 9.0 NA 5.0 5.0
## 2187 6.0 5.0 5.0 8.0 8.0
## 2188 8.0 7.0 10.0 7.0 7.0
## 2189 NA NA NA 4.0 6.0
## 2190 4.0 4.0 3.0 4.0 7.0
## 2191 4.0 2.0 2.0 7.0 8.0
## 2192 4.0 7.0 NA 5.0 6.0
## 2193 8.0 8.0 NA 8.0 7.0
## 2194 4.0 5.0 4.0 9.0 8.0
## 2195 6.0 7.0 5.0 7.0 7.0
## 2196 6.0 NA NA 7.0 7.0
## 2197 5.0 7.0 5.0 8.0 8.0
## 2198 6.0 6.0 3.0 7.0 7.0
## 2199 9.0 10.0 5.0 7.0 8.0
## 2200 7.0 6.0 7.0 6.0 8.0
## 2201 10.0 10.0 10.0 8.0 8.0
## 2202 8.0 8.0 8.0 7.0 9.0
## 2203 8.0 6.0 7.0 8.0 7.0
## 2204 7.0 7.0 5.0 7.0 9.0
## 2205 8.0 NA NA 7.0 8.0
## 2206 7.0 7.0 5.0 10.0 8.0
## 2207 5.0 5.0 2.0 9.0 9.0
## 2208 7.0 7.0 8.0 9.0 8.0
## 2209 9.0 9.0 7.0 7.0 8.0
## 2210 7.0 7.0 NA 9.0 9.0
## 2211 8.0 8.0 6.0 10.0 9.0
## 2212 7.0 NA 6.0 9.0 10.0
## 2213 9.0 8.0 9.0 10.0 9.0
## 2214 7.0 8.0 6.0 9.0 8.0
## 2215 8.0 9.0 10.0 8.0 8.0
## 2216 8.0 7.0 8.0 10.0 8.0
## 2217 9.0 8.0 7.0 7.0 7.0
## 2218 6.0 7.0 8.0 5.0 7.0
## 2220 7.0 7.0 10.0 6.0 7.0
## 2221 NA NA NA 5.0 7.0
## 2222 4.0 6.0 4.0 7.0 7.0
## 2223 4.0 8.0 1.0 6.0 6.0
## 2224 4.0 8.0 NA 7.0 7.0
## 2225 6.0 9.0 3.0 6.0 7.0
## 2226 3.0 7.0 2.0 8.0 7.0
## 2227 6.0 8.0 NA 5.0 7.0
## 2228 5.0 NA 4.0 5.0 7.0
## 2229 5.0 9.0 6.0 6.0 6.0
## 2230 3.0 8.0 6.0 6.0 7.0
## 2231 3.0 10.0 8.0 6.0 8.0
## 2232 4.0 5.0 3.0 6.0 6.0
## 2233 5.0 6.0 2.0 7.0 8.0
## 2234 7.0 8.0 8.0 5.0 8.0
## 2235 7.0 7.0 6.0 8.0 9.0
## 2236 7.0 8.0 5.0 6.0 8.0
## 2237 7.0 NA NA 6.0 8.0
## 2238 4.0 6.0 3.0 5.0 6.0
## 2239 7.0 8.0 5.0 7.0 7.0
## 2240 6.0 7.0 NA 8.0 7.0
## 2241 6.0 9.0 NA 7.0 9.0
## 2242 5.0 7.0 NA 9.0 9.0
## 2243 8.0 NA 8.0 7.0 7.0
## 2244 7.0 7.0 NA 6.0 8.0
## 2245 8.0 8.0 8.0 9.0 7.0
## 2246 4.0 8.0 5.0 10.0 8.0
## 2247 2.0 9.0 2.0 6.0 8.0
## 2248 6.0 7.0 5.0 9.0 8.0
## 2249 8.0 8.0 8.0 8.0 7.0
## 2250 8.0 8.0 NA NA 8.0
## 2251 7.0 8.0 7.0 4.0 7.0
## 2252 7.0 8.0 7.0 2.0 7.0
## 2253 7.0 NA NA 1.0 4.0
## 2254 10.0 8.0 8.0 8.0 4.0
## 2255 10.0 10.0 7.0 7.0 4.0
## 2256 6.0 6.0 6.0 6.0 4.0
## 2257 7.0 8.0 NA 4.0 6.0
## 2258 6.0 7.0 8.0 8.0 7.0
## 2259 8.0 9.0 NA 7.0 8.0
## 2260 6.0 7.0 NA 4.0 6.0
## 2261 10.0 10.0 8.0 7.0 8.0
## 2262 6.0 7.0 7.0 5.0 6.0
## 2263 8.0 10.0 7.0 6.0 8.0
## 2264 8.0 7.0 7.0 8.0 5.0
## 2265 9.0 9.0 9.0 8.0 7.0
## 2266 9.0 8.0 NA 4.0 6.0
## 2267 6.0 7.0 6.0 5.0 7.0
## 2268 10.0 10.0 10.0 4.0 6.0
## 2269 8.0 NA NA 5.0 6.0
## 2270 8.0 4.0 5.0 5.0 7.0
## 2271 5.0 8.0 2.0 5.0 8.0
## 2272 7.0 4.0 NA 8.0 8.0
## 2273 8.0 8.0 NA 7.0 7.0
## 2274 9.0 6.0 10.0 9.0 8.0
## 2275 6.0 7.0 7.0 5.0 5.0
## 2276 6.0 NA NA 5.0 5.0
## 2277 9.0 9.0 7.0 6.0 7.0
## 2278 4.0 8.0 6.0 6.0 7.0
## 2279 9.0 9.0 7.0 7.0 7.0
## 2280 6.0 5.0 6.0 7.0 8.0
## 2281 8.0 6.0 7.0 5.0 6.0
## 2282 6.0 5.0 8.0 5.0 5.0
## 2283 NA 5.0 NA 5.0 5.0
## 2284 NA NA 10.0 5.0 5.0
## 2285 5.0 5.0 5.0 5.0 5.0
## 2286 5.0 5.0 4.0 5.0 5.0
## 2287 8.0 4.0 NA 5.0 5.0
## 2288 6.0 6.0 8.0 5.0 6.0
## 2289 4.0 6.0 3.0 6.0 5.0
## 2290 5.0 6.0 6.0 6.0 5.0
## 2291 7.0 10.0 8.0 7.0 7.0
## 2292 4.0 7.0 6.0 2.0 7.0
## 2293 6.0 5.0 7.0 2.0 8.0
## 2294 5.0 NA NA 4.0 6.0
## 2295 5.0 5.0 5.0 4.0 7.0
## 2296 5.0 4.0 5.0 4.0 7.0
## 2297 9.0 8.0 10.0 7.0 8.0
## 2298 7.0 8.0 8.0 1.0 7.0
## 2299 6.0 4.0 5.0 1.0 5.0
## 2300 6.0 8.0 9.0 4.0 6.0
## 2301 7.0 10.0 6.0 7.0 7.0
## 2302 4.0 7.0 6.0 6.0 5.0
## 2303 6.0 7.0 8.0 7.0 8.0
## 2304 NA NA NA 5.0 5.0
## 2305 5.0 5.0 7.0 7.0 5.0
## 2306 6.0 4.0 5.0 6.0 7.0
## 2307 7.0 9.0 3.0 8.0 7.0
## 2308 7.0 8.0 7.0 8.0 6.0
## 2309 3.0 9.0 3.0 8.0 7.0
## 2310 3.0 9.0 6.0 7.0 8.0
## 2311 10.0 10.0 10.0 5.0 7.0
## 2312 8.0 9.0 5.0 2.0 3.0
## 2313 8.0 5.0 2.0 2.0 6.0
## 2314 NA NA NA 4.0 6.0
## 2315 5.0 5.0 5.0 1.0 6.0
## 2316 5.0 9.0 5.0 4.0 6.0
## 2317 8.0 5.0 NA 6.0 7.0
## 2318 6.0 6.0 6.0 7.0 7.0
## 2319 7.0 9.0 4.0 7.0 7.0
## 2320 6.0 8.0 7.0 5.0 7.0
## 2321 8.0 9.0 7.0 5.0 7.0
## 2322 6.0 8.0 9.0 7.0 7.0
## 2323 2.0 NA 5.0 5.0 7.0
## 2325 5.0 5.0 5.0 5.0 6.0
## 2326 5.0 5.0 6.0 7.0 7.0
## 2327 9.0 NA 9.0 7.0 5.0
## 2328 7.0 5.0 5.0 8.0 7.0
## 2329 6.0 6.0 4.0 6.0 6.0
## 2330 6.0 10.0 9.0 7.0 8.0
## 2331 10.0 10.0 5.0 3.0 7.0
## 2332 9.0 9.0 10.0 4.0 6.0
## 2333 8.0 7.0 6.0 4.0 8.0
## 2334 8.0 NA NA 4.0 8.0
## 2335 6.0 5.0 6.0 4.0 8.0
## 2336 8.0 8.0 7.0 4.0 8.0
## 2337 9.0 9.0 5.0 7.0 6.0
## 2338 8.0 8.0 6.0 4.0 7.0
## 2339 8.0 6.0 4.0 6.0 6.0
## 2340 8.0 8.0 8.0 5.0 7.0
## 2341 8.0 9.0 9.0 7.0 8.0
## 2342 8.0 8.0 5.0 4.0 4.0
## 2343 NA NA NA 6.0 7.0
## 2344 7.0 NA NA 5.0 5.0
## 2345 8.0 5.0 8.0 7.0 8.0
## 2346 6.0 6.0 5.0 7.0 6.0
## 2347 7.0 NA NA 8.0 7.0
## 2348 7.0 8.0 7.0 8.0 7.0
## 2349 8.0 6.0 4.0 9.0 8.0
## 2350 6.0 7.0 8.0 7.0 7.0
## 2351 8.0 10.0 9.0 8.0 9.0
## 2352 8.0 8.0 5.0 6.0 8.0
## 2353 NA 8.0 NA 8.0 8.0
## 2354 NA NA NA 4.0 4.0
## 2355 6.0 2.0 5.0 4.0 4.0
## 2356 6.0 7.0 6.0 5.0 10.0
## 2357 9.0 NA NA 5.0 5.0
## 2358 8.0 8.0 5.0 8.0 9.0
## 2359 5.0 6.0 3.0 6.0 4.0
## 2360 6.0 9.0 9.0 8.0 8.0
## 2361 8.0 9.0 6.0 7.0 8.0
## 2362 9.0 9.0 5.0 7.0 7.0
## 2363 5.0 5.0 NA 6.0 8.0
## 2364 NA NA NA 6.0 8.0
## 2365 6.0 7.0 5.0 1.0 7.0
## 2366 6.0 7.0 5.0 6.0 8.0
## 2367 7.0 NA NA 7.0 8.0
## 2368 7.0 8.0 5.0 9.0 9.0
## 2369 6.0 7.0 3.0 8.0 9.0
## 2370 6.0 8.0 6.0 5.0 8.0
## 2371 10.0 10.0 10.0 8.0 9.0
## 2372 7.0 6.0 10.0 7.0 6.0
## 2373 6.0 5.0 3.0 8.0 8.0
## 2374 1.0 NA 1.0 6.0 6.0
## 2375 5.0 5.0 5.0 6.0 7.0
## 2376 7.0 7.0 6.0 6.0 8.0
## 2377 8.0 4.0 1.0 7.0 7.0
## 2378 6.0 6.0 6.0 7.0 7.0
## 2379 5.0 5.0 6.0 8.0 8.0
## 2380 7.0 6.0 6.0 7.0 8.0
## 2381 6.0 6.0 6.0 8.0 10.0
## 2382 7.0 7.0 7.0 7.0 10.0
## 2383 6.0 7.0 5.0 10.0 10.0
## 2384 5.0 6.0 5.0 8.0 10.0
## 2385 5.0 NA 4.0 8.0 10.0
## 2386 4.0 6.0 7.0 10.0 10.0
## 2387 8.0 7.0 6.0 10.0 9.0
## 2388 9.0 10.0 7.0 10.0 10.0
## 2389 8.0 9.0 8.0 8.0 10.0
## 2390 7.0 6.0 9.0 10.0 10.0
## 2391 5.0 5.0 NA 6.0 6.0
## 2392 5.0 7.0 2.0 3.0 8.0
## 2393 6.0 7.0 4.0 3.0 8.0
## 2394 3.0 5.0 2.0 2.0 9.0
## 2395 6.0 NA NA 3.0 7.0
## 2396 5.0 6.0 6.0 8.0 8.0
## 2397 3.0 3.0 3.0 9.0 10.0
## 2398 6.0 10.0 6.0 9.0 10.0
## 2399 9.0 8.0 8.0 6.0 8.0
## 2400 6.0 6.0 7.0 6.0 6.0
## 2401 6.0 6.0 6.0 4.0 6.0
## 2402 7.0 8.0 6.0 7.0 6.0
## 2403 6.0 5.0 6.0 8.0 7.0
## 2404 2.0 4.0 2.0 5.0 5.0
## 2405 5.0 NA NA 5.0 6.0
## 2406 6.0 6.0 7.0 7.0 7.0
## 2407 5.0 5.0 6.0 7.0 7.0
## 2408 7.0 9.0 7.0 6.0 8.0
## 2409 9.0 10.0 8.0 5.0 7.0
## 2410 7.0 6.0 5.0 7.0 7.0
## 2411 5.0 5.0 7.0 6.0 8.0
## 2412 7.0 6.0 7.0 6.0 NA
## 2414 5.0 NA 4.0 4.0 NA
## 2416 6.0 6.0 7.0 7.0 7.0
## 2417 5.0 5.0 4.0 NA NA
## 2419 8.0 9.0 9.0 NA NA
## 2420 6.0 6.0 6.0 4.0 NA
## 2421 5.0 5.0 5.0 4.0 5.0
## 2422 7.0 7.0 7.0 6.0 5.0
## 2423 8.0 6.0 4.0 6.0 5.0
## 2424 3.0 4.0 2.0 3.0 4.0
## 2425 5.0 NA NA 5.0 5.0
## 2426 6.0 7.0 6.0 7.0 6.0
## 2427 7.0 8.0 5.0 8.0 8.0
## 2428 4.0 4.0 4.0 6.0 7.0
## 2429 9.0 10.0 9.0 5.0 7.0
## 2430 7.0 7.0 6.0 4.0 5.0
## 2431 5.0 5.0 5.0 5.0 8.0
## 2432 6.0 6.0 7.0 6.0 6.0
## 2433 5.0 5.0 3.0 8.0 8.0
## 2434 3.0 5.0 4.0 5.0 6.0
## 2435 5.0 NA NA 5.0 8.0
## 2436 5.0 7.0 7.0 7.0 8.0
## 2437 6.0 6.0 5.0 6.0 8.0
## 2438 8.0 9.0 4.0 6.0 7.0
## 2439 8.0 9.0 8.0 5.0 7.0
## 2440 7.0 6.0 9.0 6.0 8.0
## 2441 6.0 6.0 6.0 5.0 8.0
## 2442 7.0 5.0 7.0 9.0 8.0
## 2443 8.0 6.0 2.0 9.0 NA
## 2444 8.0 8.0 3.0 5.0 NA
## 2445 7.0 5.0 NA 3.0 9.0
## 2446 7.0 7.0 7.0 10.0 8.0
## 2447 8.0 7.0 6.0 7.0 5.0
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## 2999 8.0 9.0 6.0 8.0 8.0
## 3000 8.0 10.0 8.0 7.0 6.0
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## 3222 8.0 10.0 9.0 6.0 8.0
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## 3225 4.0 4.0 5.0 3.0 6.0
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## 3229 9.0 8.0 6.0 6.0 6.0
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## 3246 2.0 7.0 2.0 6.0 5.0
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## 3252 6.0 8.0 8.0 8.0 9.0
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## 3261 7.0 8.0 6.0 6.0 7.0
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## 3266 6.0 8.0 2.0 9.0 7.0
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## 3271 6.0 8.0 7.0 7.0 8.0
## 3272 7.0 9.0 8.0 7.0 7.0
## 3273 7.0 7.0 7.0 7.0 7.0
## 3274 9.0 7.0 8.0 9.0 8.0
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## 3279 7.0 7.0 7.0 8.0 8.0
## 3280 8.0 7.0 7.0 8.0 8.0
## 3281 7.0 8.0 NA 4.0 8.0
## 3282 8.0 9.0 3.0 7.0 8.0
## 3283 6.0 NA 5.0 4.0 6.0
## 3284 NA NA NA 4.0 8.0
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## 3287 9.0 7.0 9.0 4.0 8.0
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## 3291 8.0 8.0 5.0 6.0 7.0
## 3292 5.0 4.0 4.0 2.0 6.0
## 3293 5.0 NA 4.0 3.0 7.0
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## 3300 2.0 2.0 1.0 7.0 7.0
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## 3450 7.0 8.0 5.0 1.0 9.0
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## 3452 7.0 8.0 7.0 1.0 2.0
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## 3454 7.0 NA NA 2.0 4.0
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## 3456 7.0 9.0 NA 4.0 9.0
## 3457 7.0 6.0 NA 1.0 2.0
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## 3487 6.0 5.0 6.0 8.0 7.0
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## 3491 7.0 8.0 4.0 9.0 8.0
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## 3494 7.0 7.0 5.0 7.0 6.0
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## 3500 5.0 5.0 5.0 5.0 9.0
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## 3558 10.0 10.0 10.0 4.0 7.0
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## 3572 8.0 8.0 5.0 7.0 10.0
## 3573 8.0 NA NA 8.0 9.0
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## 3576 9.0 6.0 6.0 5.0 9.0
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## 3582 NA NA 8.0 5.0 4.0
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## 3591 5.0 6.0 5.0 5.0 6.0
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## 3594 9.0 NA NA 7.0 3.0
## 3595 3.0 5.0 5.0 7.0 7.0
## 3596 3.0 4.0 2.0 5.0 1.0
## 3597 7.0 8.0 6.0 5.0 7.0
## 3598 4.0 9.0 8.0 7.0 8.0
## 3599 7.0 7.0 NA 7.0 7.0
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## 3613 5.0 7.0 8.0 6.0 8.0
## 3614 7.0 5.0 6.0 7.0 7.0
## 3615 8.0 NA NA 9.0 9.0
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## 3617 8.0 7.0 5.0 9.0 7.0
## 3618 7.0 8.0 7.0 7.0 9.0
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## 3621 7.0 7.0 7.0 4.0 7.0
## 3622 6.0 NA NA 7.0 8.0
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## 3634 5.0 6.0 7.0 8.0 8.0
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## 3659 3.0 7.0 4.0 7.0 7.0
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## 3666 7.0 NA NA 7.0 9.0
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## 3668 6.0 5.0 5.0 9.0 8.0
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## 3676 5.0 7.0 7.0 5.0 10.0
## 3677 8.0 7.0 8.0 5.0 9.0
## 3678 8.0 NA 3.0 8.0 8.0
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## 3682 7.0 8.0 6.0 7.0 4.0
## 3683 8.0 8.0 8.0 9.0 9.0
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## 3689 7.0 6.0 7.0 5.0 10.0
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## 3697 7.0 5.0 6.0 5.0 6.0
## 3698 8.0 5.0 7.0 5.0 6.0
## 3699 7.0 NA NA 6.0 6.0
## 3700 5.0 5.0 2.0 3.0 6.0
## 3701 7.0 6.0 6.0 7.0 8.0
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## 4156 4.0 7.0 4.0 8.0 8.0
## 4157 2.0 4.0 1.0 9.0 8.0
## 4158 6.0 7.0 5.0 6.0 NA
## 4159 3.0 5.0 3.0 7.0 NA
## 4160 5.0 7.0 5.0 7.0 NA
## 4161 6.0 5.0 5.0 6.0 NA
## 4162 5.0 7.0 6.0 7.0 NA
## 4163 2.0 3.0 3.0 8.0 NA
## 4164 4.0 8.0 3.0 7.0 NA
## 4165 5.0 5.0 5.0 7.0 NA
## 4166 5.0 5.0 3.0 6.0 NA
## 4167 7.0 6.0 5.0 7.0 NA
## 4168 9.0 10.0 5.0 8.0 7.0
## 4169 7.0 5.0 5.0 8.0 NA
## 4170 4.0 8.0 6.0 7.0 6.0
## 4171 3.0 NA 5.0 7.0 NA
## 4172 6.0 7.0 4.0 6.0 NA
## 4173 6.0 6.0 4.0 10.0 NA
## 4174 6.0 7.0 4.0 7.0 NA
## 4175 4.0 6.0 5.0 7.0 NA
## 4176 4.0 5.0 4.0 8.0 NA
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## 4187 5.0 6.0 4.0 4.0 6.0
## 4188 6.0 9.0 4.0 6.0 8.0
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## 4191 5.0 6.0 3.0 5.0 9.0
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## 4229 9.0 8.0 6.0 4.0 2.0
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## 4232 8.0 7.0 8.0 8.0 8.0
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## 4245 6.0 5.0 7.0 7.0 8.0
## 4246 7.0 7.0 6.0 6.0 7.0
## 4247 6.0 7.0 NA 7.0 7.0
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## 4249 6.0 8.0 5.0 4.0 6.0
## 4250 6.0 6.0 8.0 5.0 7.0
## 4251 6.0 7.0 5.0 3.0 5.0
## 4252 9.0 8.0 7.0 5.0 7.0
## 4253 7.0 7.0 5.0 3.0 5.0
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## 4271 7.0 8.0 6.0 6.0 7.0
## 4272 7.0 8.0 6.0 5.0 10.0
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## 4278 7.0 9.0 8.0 9.0 9.0
## 4279 8.0 9.0 8.0 6.0 8.0
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## 4287 7.0 6.0 5.0 6.0 10.0
## 4288 7.0 6.0 6.0 9.0 10.0
## 4289 9.0 8.0 NA 7.0 9.0
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## 4291 8.0 9.0 NA 5.0 10.0
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## 4295 7.0 6.0 6.0 6.0 10.0
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## 4303 7.0 8.0 7.0 6.0 10.0
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## 4307 6.0 6.0 6.0 5.0 7.0
## 4308 4.0 7.0 5.0 6.0 8.0
## 4309 5.0 5.0 6.0 7.0 7.0
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## 4324 6.0 6.0 6.0 7.0 8.0
## 4325 8.0 7.0 8.0 4.0 9.0
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## 4335 6.0 5.0 5.0 3.0 8.0
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## 4338 7.0 5.0 5.0 5.0 7.0
## 4339 8.0 7.0 7.0 4.0 10.0
## 4340 10.0 8.0 NA 6.0 8.0
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## 4342 5.0 7.0 8.0 7.0 7.0
## 4343 3.0 4.0 2.0 5.0 7.0
## 4344 7.0 NA 9.0 7.0 7.0
## 4345 5.0 5.0 4.0 6.0 8.0
## 4346 7.0 4.0 3.0 7.0 7.0
## 4347 6.0 5.0 3.0 5.0 7.0
## 4348 4.0 7.0 4.0 8.0 6.0
## 4349 6.0 4.0 7.0 5.0 9.0
## 4350 6.0 6.0 7.0 4.0 8.0
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## 4353 7.0 6.0 7.0 5.0 5.0
## 4354 8.0 10.0 4.0 7.0 8.0
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## 4358 4.0 8.0 4.0 8.0 8.0
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## 4362 9.0 9.0 6.0 8.0 10.0
## 4363 5.0 5.0 6.0 5.0 5.0
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## 4366 6.0 7.0 6.0 8.0 7.0
## 4367 8.0 6.0 7.0 5.0 10.0
## 4368 8.0 NA 8.0 7.0 8.0
## 4369 8.0 7.0 6.0 7.0 8.0
## 4370 6.0 5.0 8.0 5.0 9.0
## 4371 6.0 3.0 2.0 5.0 5.0
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## 4375 7.0 7.0 5.0 7.0 7.0
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## 4377 6.0 7.0 5.0 3.0 8.0
## 4378 8.0 9.0 8.0 3.0 10.0
## 4379 5.0 5.0 NA 3.0 7.0
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## 4382 8.0 8.0 4.0 7.0 9.0
## 4383 8.0 6.0 4.0 7.0 7.0
## 4384 7.0 5.0 6.0 8.0 9.0
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## 4391 6.0 6.0 5.0 6.0 7.0
## 4392 5.0 6.0 5.0 6.0 7.0
## 4393 5.0 5.0 NA 6.0 7.0
## 4394 6.0 6.0 5.0 7.0 7.0
## 4395 8.0 6.0 8.0 8.0 9.0
## 4396 9.0 8.0 NA 7.0 8.0
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## 4398 4.0 7.0 5.0 10.0 9.0
## 4399 7.0 6.0 3.0 8.0 9.0
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## 4401 5.0 6.0 4.0 8.0 9.0
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## 4403 8.0 5.0 5.0 10.0 10.0
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## 4405 7.0 4.0 5.0 7.0 9.0
## 4406 10.0 9.0 9.0 7.0 8.0
## 4407 6.0 5.0 NA 7.0 9.0
## 4408 6.0 6.0 5.0 6.0 7.0
## 4409 7.0 6.0 7.0 6.0 10.0
## 4410 9.0 8.0 NA 10.0 10.0
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## 4412 4.0 5.0 7.0 7.0 10.0
## 4413 4.0 4.0 2.0 8.0 10.0
## 4414 8.0 NA 6.0 9.0 10.0
## 4415 4.0 5.0 4.0 4.0 9.0
## 4416 6.0 6.0 3.0 8.0 10.0
## 4417 7.0 7.0 8.0 8.0 9.0
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## 4419 6.0 5.0 5.0 7.0 10.0
## 4420 8.0 9.0 8.0 8.0 10.0
## 4421 2.0 2.0 NA 8.0 9.0
## 4422 5.0 5.0 5.0 9.0 8.0
## 4423 7.0 6.0 8.0 5.0 6.0
## 4424 8.0 10.0 7.0 6.0 7.0
## 4425 6.0 7.0 4.0 3.0 7.0
## 4426 8.0 7.0 8.0 7.0 8.0
## 4427 5.0 8.0 3.0 5.0 6.0
## 4428 8.0 8.0 5.0 7.0 7.0
## 4429 7.0 5.0 6.0 7.0 6.0
## 4430 7.0 7.0 3.0 5.0 6.0
## 4431 4.0 5.0 3.0 6.0 6.0
## 4432 8.0 5.0 5.0 9.0 8.0
## 4433 6.0 7.0 6.0 5.0 10.0
## 4434 8.0 9.0 10.0 5.0 6.0
## 4435 2.0 9.0 NA 5.0 6.0
## 4436 7.0 5.0 5.0 6.0 6.0
## 4437 8.0 7.0 7.0 5.0 10.0
## 4438 4.0 4.0 0.0 4.0 9.0
## 4439 7.0 5.0 4.0 3.0 NA
## 4440 0.0 3.0 8.0 7.0 9.0
## 4441 8.0 4.0 8.0 10.0 10.0
## 4442 8.0 6.0 5.0 6.0 10.0
## 4443 7.0 6.0 6.0 5.0 7.0
## 4444 4.0 7.0 2.0 7.0 6.0
## 4445 8.0 7.0 6.0 7.0 6.0
## 4446 6.0 7.0 4.0 7.0 10.0
## 4447 4.0 6.0 4.0 7.0 8.0
## 4448 6.0 8.0 5.0 2.0 10.0
## 4449 10.0 5.0 NA 6.0 9.0
## 4450 8.0 7.0 7.0 7.0 6.0
## 4451 7.0 6.0 6.0 4.0 6.0
## 4452 9.0 10.0 NA 7.0 8.0
## 4453 5.0 5.0 5.0 7.0 5.0
## 4454 8.0 5.0 8.0 8.0 7.0
## 4455 6.0 7.0 9.0 6.0 10.0
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## 4461 5.0 5.0 5.0 5.0 7.0
## 4462 9.0 8.0 10.0 3.0 6.0
## 4463 5.0 5.0 NA 5.0 8.0
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## 4466 10.0 8.0 4.0 6.0 7.0
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## 4474 8.0 8.0 7.0 7.0 7.0
## 4475 6.0 6.0 3.0 5.0 6.0
## 4476 9.0 8.0 8.0 3.0 6.0
## 4477 5.0 5.0 NA 5.0 6.0
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## 4479 7.0 6.0 7.0 5.0 8.0
## 4480 10.0 10.0 5.0 7.0 9.0
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## 4489 6.0 5.0 4.0 4.0 7.0
## 4490 5.0 8.0 6.0 4.0 6.0
## 4491 5.0 9.0 NA 7.0 7.0
## 4492 6.0 5.0 5.0 7.0 6.0
## 4493 10.0 8.0 7.0 9.0 9.0
## 4494 8.0 NA NA 8.0 9.0
## 4495 6.0 7.0 3.0 7.0 6.0
## 4496 2.0 8.0 7.0 8.0 8.0
## 4497 5.0 5.0 2.0 7.0 8.0
## 4498 6.0 6.0 4.0 7.0 8.0
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## 4501 3.0 4.0 2.0 7.0 8.0
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## 4503 4.0 5.0 4.0 5.0 6.0
## 4504 6.0 5.0 5.0 5.0 6.0
## 4505 4.0 4.0 NA 6.0 6.0
## 4506 7.0 5.0 6.0 6.0 6.0
## 4507 8.0 7.0 7.0 3.0 6.0
## 4508 10.0 NA NA 4.0 7.0
## 4509 8.0 8.0 5.0 4.0 6.0
## 4510 7.0 5.0 6.0 7.5 8.0
## 4511 6.0 7.0 6.0 7.0 8.0
## 4512 8.0 8.0 8.0 8.0 9.0
## 4513 7.0 5.0 6.0 4.0 8.0
## 4514 9.0 5.0 4.0 6.5 8.0
## 4515 6.0 6.0 4.0 NA 7.0
## 4516 6.0 8.0 9.0 7.0 6.0
## 4517 6.0 5.0 3.0 4.0 6.0
## 4518 7.0 5.0 6.0 3.0 8.0
## 4519 4.0 6.0 NA 5.0 6.0
## 4520 8.0 7.0 7.0 4.0 7.0
## 4521 6.0 8.0 6.0 9.0 9.0
## 4522 6.0 6.0 NA 8.0 9.0
## 4523 3.0 3.0 4.0 7.0 8.0
## 4524 7.0 8.0 8.0 8.0 9.0
## 4525 6.0 8.0 6.0 9.0 8.0
## 4526 10.0 10.0 5.0 8.0 9.0
## 4527 9.0 10.0 8.0 6.0 8.0
## 4528 2.0 9.0 2.0 7.0 9.0
## 4529 NA 10.0 5.0 8.0 9.0
## 4530 5.0 8.0 6.0 8.0 9.0
## 4531 1.0 8.0 0.0 7.0 9.0
## 4532 7.0 7.0 6.0 8.0 9.0
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## 4534 5.0 5.0 4.0 8.0 9.0
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## 4946 8.0 7.0 5.0 8.0 9.0
## 4947 4.0 3.0 1.0 4.0 1.0
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## 4949 3.0 2.0 1.0 6.0 10.0
## 4950 5.0 8.0 5.0 7.0 10.0
## 4951 4.0 3.0 3.0 9.0 NA
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## 1349 10.0 NA NA
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## 1351 8.0 5.0 7.0
## 1352 9.0 8.0 8.0
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## 1354 8.0 8.0 8.0
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## 1387 8.0 8.0 NA
## 1388 6.0 5.0 NA
## 1389 6.0 5.0 NA
## 1390 8.0 6.0 NA
## 1391 7.0 NA NA
## 1392 7.0 7.0 NA
## 1393 8.0 9.0 7.0
## 1394 8.0 7.0 NA
## 1395 8.0 7.0 NA
## 1396 7.0 8.0 NA
## 1397 8.0 9.0 NA
## 1398 6.0 6.0 NA
## 1399 8.0 7.0 NA
## 1400 9.0 6.0 9.0
## 1401 9.0 6.0 8.0
## 1402 7.0 8.0 NA
## 1403 8.0 7.0 NA
## 1404 8.0 8.0 NA
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## 1407 6.0 3.0 6.0
## 1408 4.0 4.0 6.0
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## 1488 6.0 8.0 4.0
## 1489 8.0 8.0 6.0
## 1490 8.0 8.0 10.0
## 1491 8.0 3.0 NA
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## 1494 10.0 8.0 8.0
## 1495 9.0 9.0 9.0
## 1496 8.0 8.0 9.0
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## 1512 9.0 9.5 9.5
## 1513 8.0 7.0 5.0
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## 1560 8.0 7.0 9.0
## 1561 8.0 7.0 6.0
## 1562 8.0 5.0 9.0
## 1563 8.0 4.0 NA
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## 1566 8.0 7.0 8.0
## 1567 8.0 8.0 6.0
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## 1579 7.0 5.0 5.0
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## 1598 8.0 10.0 7.0
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## 1604 5.0 6.0 5.0
## 1605 5.0 5.0 5.0
## 1606 2.0 2.0 6.0
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## 1609 10.0 10.0 10.0
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## 1611 10.0 2.0 10.0
## 1612 10.0 10.0 10.0
## 1613 10.0 8.0 10.0
## 1614 9.0 9.0 10.0
## 1615 9.0 8.0 8.0
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## 1620 8.0 9.0 8.0
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## 1631 6.0 6.0 6.0
## 1633 5.0 5.0 5.0
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## 1635 8.0 NA 8.0
## 1636 8.0 9.0 8.0
## 1637 8.0 7.0 6.0
## 1638 9.0 10.0 8.0
## 1639 NA NA NA
## 1640 6.0 2.0 NA
## 1641 7.0 8.0 8.0
## 1642 6.0 2.0 2.0
## 1643 4.0 1.0 6.0
## 1644 8.0 6.0 8.0
## 1645 8.0 7.0 8.0
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## 1676 7.0 5.0 5.0
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## 1690 6.0 6.0 6.0
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## 1704 7.0 2.0 5.0
## 1705 7.0 10.0 7.0
## 1706 7.0 7.0 5.0
## 1707 8.0 8.0 10.0
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## 1710 7.0 0.0 5.0
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## 1712 9.0 8.0 8.0
## 1713 9.0 8.0 8.0
## 1714 6.0 10.0 8.0
## 1715 7.0 7.0 8.0
## 1716 9.0 9.0 7.0
## 1717 9.0 7.0 8.0
## 1718 10.0 10.0 8.0
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## 1721 5.0 5.0 5.0
## 1722 5.0 5.0 5.0
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## 1726 6.0 6.0 6.0
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## 1729 7.0 9.0 6.0
## 1730 8.0 8.0 8.0
## 1731 9.0 5.0 9.0
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## 1734 7.0 8.0 6.0
## 1735 8.0 7.0 8.0
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## 1738 5.0 7.0 5.0
## 1739 7.0 9.0 10.0
## 1740 9.0 8.0 7.0
## 1741 8.0 5.0 8.0
## 1742 9.0 9.0 10.0
## 1743 6.0 5.0 6.0
## 1744 NA NA NA
## 1745 8.0 NA NA
## 1746 NA 8.0 4.0
## 1747 8.0 8.0 NA
## 1748 NA 8.0 8.0
## 1749 9.0 8.0 9.0
## 1750 10.0 6.0 7.0
## 1751 9.0 8.0 8.0
## 1752 8.0 7.0 7.0
## 1753 4.0 6.0 6.0
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## 1756 6.0 1.0 7.0
## 1757 8.0 8.0 7.0
## 1758 10.0 6.0 8.0
## 1759 9.0 7.0 6.0
## 1760 NA 6.0 5.0
## 1761 7.0 7.0 7.0
## 1762 8.0 6.0 8.0
## 1763 7.5 7.0 7.0
## 1764 8.0 7.0 7.0
## 1765 7.0 7.0 7.0
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## 1768 8.0 8.0 6.0
## 1769 6.0 4.0 6.0
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## 1773 9.0 8.0 8.0
## 1774 8.0 8.0 8.0
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## 1778 8.0 8.0 8.0
## 1779 9.0 10.0 9.0
## 1780 10.0 10.0 10.0
## 1781 7.0 9.0 8.0
## 1782 9.0 5.0 6.0
## 1783 8.0 8.0 8.0
## 1784 8.0 9.0 9.0
## 1785 8.0 6.0 7.0
## 1786 8.0 5.0 NA
## 1787 7.0 8.0 7.0
## 1788 8.0 9.0 8.0
## 1789 9.0 8.0 9.0
## 1790 9.0 8.0 9.0
## 1791 9.0 8.0 8.0
## 1792 9.0 10.0 6.0
## 1793 9.0 8.0 8.0
## 1794 7.0 6.0 4.0
## 1795 9.0 9.0 9.0
## 1796 9.0 8.0 8.0
## 1797 9.0 6.0 7.0
## 1798 9.0 7.0 8.0
## 1799 9.0 8.0 8.0
## 1800 8.0 9.0 8.0
## 1801 7.0 4.0 7.0
## 1802 8.0 5.0 7.0
## 1803 8.0 8.0 8.0
## 1804 8.0 10.0 7.0
## 1805 8.0 8.0 7.0
## 1806 7.0 8.0 7.0
## 1807 7.0 7.0 6.0
## 1808 6.0 5.0 6.0
## 1809 8.0 7.0 7.0
## 1810 8.0 7.0 7.0
## 1811 6.0 6.0 5.0
## 1812 8.0 8.0 6.0
## 1813 8.0 6.0 7.0
## 1814 8.0 7.0 7.0
## 1815 7.0 7.0 8.0
## 1816 8.0 6.0 7.0
## 1817 10.0 7.0 9.0
## 1818 10.0 10.0 10.0
## 1819 7.0 10.0 7.0
## 1820 9.0 9.0 8.0
## 1821 9.0 8.0 9.0
## 1822 8.0 7.0 9.0
## 1823 10.0 9.0 8.0
## 1824 7.0 7.0 5.0
## 1825 7.0 7.0 7.0
## 1826 9.0 8.0 7.0
## 1827 8.0 8.0 7.0
## 1828 9.0 7.0 7.0
## 1829 9.0 7.0 7.0
## 1830 9.0 7.0 8.0
## 1831 9.0 7.0 8.0
## 1832 10.0 10.0 10.0
## 1833 8.0 NA NA
## 1834 10.0 8.0 NA
## 1835 7.0 8.0 8.0
## 1836 8.0 NA NA
## 1837 10.0 NA 8.0
## 1838 8.0 8.0 NA
## 1839 10.0 10.0 8.0
## 1840 7.0 NA NA
## 1841 6.0 6.0 NA
## 1842 8.0 7.0 NA
## 1843 10.0 NA NA
## 1844 8.0 8.0 NA
## 1845 8.0 NA NA
## 1846 8.0 7.0 NA
## 1847 8.0 7.0 NA
## 1848 8.0 8.0 NA
## 1849 8.0 5.0 5.0
## 1850 8.0 5.0 5.0
## 1851 7.0 8.0 9.0
## 1852 7.0 6.0 6.0
## 1853 6.0 6.0 5.0
## 1854 8.0 7.0 7.0
## 1855 8.0 6.0 6.0
## 1856 7.0 5.0 6.0
## 1857 8.0 6.0 8.0
## 1858 6.0 6.0 6.0
## 1859 8.0 4.0 4.0
## 1860 8.0 7.0 7.0
## 1861 8.0 4.0 6.0
## 1862 9.0 4.0 6.0
## 1863 10.0 10.0 8.0
## 1864 8.0 8.0 4.0
## 1865 8.0 2.0 6.0
## 1866 8.0 2.0 7.0
## 1867 7.0 8.0 8.0
## 1868 8.0 5.0 5.0
## 1869 8.0 4.0 9.0
## 1870 9.0 8.0 8.0
## 1871 7.0 6.0 7.0
## 1872 6.0 1.0 5.0
## 1873 7.0 5.0 5.0
## 1874 8.0 5.0 8.0
## 1875 8.0 4.0 2.0
## 1876 8.0 5.0 5.0
## 1877 8.0 4.0 8.0
## 1878 8.0 7.0 8.0
## 1879 8.0 10.0 10.0
## 1880 9.0 5.0 8.0
## 1881 7.0 4.0 7.0
## 1882 8.0 4.0 6.0
## 1883 8.0 8.0 8.0
## 1884 8.0 8.0 6.0
## 1885 7.0 4.0 8.0
## 1886 6.0 4.0 4.0
## 1887 8.0 3.0 3.0
## 1888 6.0 4.0 4.0
## 1889 6.0 3.0 7.0
## 1890 7.0 7.0 5.0
## 1891 8.0 4.0 7.0
## 1892 8.0 7.0 7.0
## 1893 7.0 4.0 8.0
## 1894 8.0 6.0 7.0
## 1895 7.0 6.0 6.0
## 1896 8.0 7.0 4.0
## 1897 8.0 6.0 9.0
## 1898 10.0 8.0 8.0
## 1899 7.0 7.0 7.0
## 1900 8.0 8.0 8.0
## 1901 7.0 6.0 6.0
## 1902 8.0 8.0 6.0
## 1903 6.0 7.0 6.0
## 1904 8.0 7.0 8.0
## 1905 6.0 5.0 8.0
## 1906 8.0 8.0 8.0
## 1907 8.0 8.0 8.0
## 1908 9.0 9.0 9.0
## 1909 9.0 6.0 9.0
## 1910 9.0 6.0 9.0
## 1911 8.0 7.0 8.0
## 1912 8.0 8.0 8.0
## 1913 5.0 1.0 3.0
## 1914 7.0 4.0 6.0
## 1915 8.0 7.0 7.0
## 1916 8.0 8.0 7.0
## 1917 7.0 7.0 6.0
## 1918 7.0 8.0 7.0
## 1919 8.0 5.0 7.0
## 1920 5.0 4.0 6.0
## 1921 6.0 6.0 5.0
## 1922 6.0 5.0 4.0
## 1923 7.0 4.0 5.0
## 1924 7.0 7.0 7.0
## 1925 8.0 3.0 7.0
## 1926 6.0 5.0 7.0
## 1927 7.0 6.0 7.0
## 1928 8.0 9.0 6.0
## 1929 7.0 6.0 8.0
## 1930 5.0 4.0 NA
## 1931 7.0 7.0 7.0
## 1932 8.0 8.0 9.0
## 1933 8.0 7.0 NA
## 1934 7.0 7.0 6.0
## 1935 8.0 7.0 NA
## 1936 7.0 8.0 8.0
## 1937 6.0 5.0 NA
## 1938 8.0 8.0 8.0
## 1939 7.0 6.0 7.0
## 1940 9.0 8.0 8.0
## 1941 7.0 6.0 8.0
## 1942 8.0 8.0 NA
## 1943 7.0 8.0 9.0
## 1944 8.0 6.0 7.0
## 1945 8.0 6.0 NA
## 1946 6.0 8.0 NA
## 1947 6.0 6.0 6.0
## 1948 7.0 7.0 NA
## 1949 7.0 6.0 7.0
## 1950 8.0 8.0 6.0
## 1951 7.0 7.0 NA
## 1952 5.0 5.0 NA
## 1953 5.0 4.0 NA
## 1954 7.0 7.0 NA
## 1955 6.0 6.0 NA
## 1956 7.0 7.0 NA
## 1957 7.0 5.0 NA
## 1958 7.0 7.0 7.0
## 1959 6.0 6.0 7.0
## 1960 6.0 6.0 NA
## 1961 10.0 5.0 8.0
## 1962 9.0 8.0 9.0
## 1963 9.0 9.0 9.0
## 1964 9.0 9.0 8.0
## 1965 8.0 7.0 7.0
## 1966 8.0 9.0 8.0
## 1967 9.0 9.0 7.0
## 1968 7.0 5.0 5.0
## 1969 9.0 9.0 9.0
## 1970 8.0 8.0 8.0
## 1971 7.0 5.0 7.0
## 1972 9.0 9.0 8.0
## 1973 10.0 5.0 9.0
## 1974 9.0 8.0 8.0
## 1975 10.0 10.0 10.0
## 1976 10.0 9.0 9.0
## 1977 7.0 2.0 7.0
## 1978 6.0 3.0 6.0
## 1979 6.0 7.0 6.0
## 1980 7.0 8.0 7.0
## 1981 6.0 4.0 6.0
## 1982 6.0 6.0 5.0
## 1983 7.0 7.0 8.0
## 1984 5.0 6.0 4.0
## 1985 7.0 6.0 6.0
## 1986 6.0 6.0 6.0
## 1987 4.0 6.0 6.0
## 1988 8.0 7.0 8.0
## 1989 8.0 3.0 8.0
## 1990 7.0 4.0 8.0
## 1991 8.0 6.0 7.0
## 1992 8.0 4.0 8.0
## 1993 10.0 3.0 10.0
## 1994 10.0 6.0 10.0
## 1995 10.0 9.0 10.0
## 1996 10.0 7.0 9.0
## 1997 10.0 3.0 8.0
## 1998 10.0 7.0 10.0
## 1999 10.0 6.0 10.0
## 2000 10.0 7.0 8.0
## 2001 10.0 9.0 10.0
## 2002 10.0 6.0 9.0
## 2003 10.0 9.0 10.0
## 2004 10.0 8.0 9.0
## 2005 10.0 3.0 10.0
## 2006 10.0 2.0 9.0
## 2007 10.0 8.0 10.0
## 2008 10.0 9.0 9.0
## 2009 3.0 2.0 3.0
## 2010 6.0 6.0 5.0
## 2011 7.0 10.0 9.0
## 2012 6.0 7.0 6.0
## 2013 7.0 2.0 5.0
## 2014 6.0 5.0 5.0
## 2015 6.0 7.0 7.0
## 2016 7.0 4.0 5.0
## 2017 7.0 7.0 7.0
## 2018 5.0 6.0 3.0
## 2019 8.0 7.0 6.0
## 2020 7.0 8.0 7.0
## 2021 7.0 4.0 5.0
## 2022 7.0 6.0 7.0
## 2023 7.0 8.0 7.0
## 2024 5.0 6.0 5.0
## 2025 8.0 7.0 7.0
## 2026 9.0 6.0 8.0
## 2027 9.0 7.0 7.0
## 2028 9.0 7.0 7.0
## 2029 8.0 7.0 7.0
## 2030 7.0 9.0 8.0
## 2031 8.0 7.0 7.0
## 2032 9.0 8.0 7.0
## 2033 9.0 7.0 7.0
## 2034 8.0 9.0 7.0
## 2035 8.0 7.0 7.0
## 2036 8.0 6.0 7.0
## 2037 7.0 8.0 7.0
## 2038 9.0 8.0 7.0
## 2039 8.0 8.0 7.0
## 2040 8.0 7.0 6.0
## 2041 7.0 6.0 NA
## 2042 7.0 5.0 4.0
## 2043 8.0 8.0 7.0
## 2044 8.0 7.0 7.0
## 2045 8.0 5.0 6.0
## 2046 6.0 6.0 6.0
## 2047 6.0 5.0 5.0
## 2048 7.0 7.0 7.0
## 2049 9.0 8.0 8.0
## 2050 7.0 7.0 7.0
## 2051 7.0 5.0 NA
## 2052 7.0 6.0 6.0
## 2053 7.0 7.0 8.0
## 2054 8.0 7.0 5.0
## 2055 7.0 5.0 5.0
## 2056 6.0 6.0 NA
## 2057 8.0 7.0 9.0
## 2058 7.0 5.0 8.0
## 2059 9.0 9.0 9.0
## 2060 9.0 4.0 10.0
## 2061 NA 6.0 7.0
## 2062 9.0 8.0 8.0
## 2063 8.0 8.0 8.0
## 2064 9.0 6.0 8.0
## 2065 9.0 8.0 8.0
## 2066 NA 8.0 7.0
## 2067 10.0 6.0 9.0
## 2068 8.0 6.0 7.0
## 2069 8.0 7.0 8.0
## 2070 8.0 8.0 NA
## 2071 8.0 9.0 9.0
## 2072 8.0 9.0 9.0
## 2073 8.0 7.0 6.0
## 2074 8.0 5.0 10.0
## 2075 10.0 9.0 3.0
## 2076 10.0 7.0 5.0
## 2077 7.0 4.0 5.0
## 2078 9.0 10.0 6.0
## 2079 8.0 5.0 7.0
## 2080 7.0 5.0 8.0
## 2081 10.0 8.0 6.0
## 2082 8.0 9.0 8.0
## 2083 9.0 6.0 5.0
## 2084 6.0 6.0 5.0
## 2085 8.0 9.0 7.0
## 2086 8.0 10.0 7.0
## 2087 10.0 8.0 8.0
## 2088 8.0 9.0 7.0
## 2089 7.0 6.0 6.0
## 2090 6.0 2.0 7.0
## 2091 7.0 7.0 7.0
## 2092 7.0 7.0 6.0
## 2093 8.0 3.0 6.0
## 2094 7.0 8.0 7.0
## 2095 7.0 7.0 6.0
## 2096 6.0 7.0 7.0
## 2097 8.0 5.0 6.0
## 2098 7.0 7.0 6.0
## 2099 7.0 5.0 6.0
## 2100 6.0 3.0 7.0
## 2101 7.0 6.0 7.0
## 2102 3.0 4.0 7.0
## 2103 7.0 5.0 9.0
## 2104 6.0 6.0 7.0
## 2105 7.0 5.0 6.0
## 2106 7.0 6.0 6.0
## 2107 8.0 6.0 6.0
## 2108 6.0 8.0 5.0
## 2109 7.0 6.0 7.0
## 2110 7.0 8.0 7.0
## 2111 8.0 7.0 6.0
## 2112 8.0 7.0 7.0
## 2113 9.0 8.0 8.0
## 2114 8.0 7.0 6.0
## 2115 8.0 6.0 7.0
## 2116 8.0 8.0 8.0
## 2117 8.0 8.0 7.0
## 2118 6.0 6.0 6.0
## 2119 9.0 8.0 8.0
## 2120 6.0 6.0 7.0
## 2121 9.0 7.0 NA
## 2122 9.0 5.0 NA
## 2123 9.0 7.0 6.0
## 2124 9.0 8.0 NA
## 2125 10.0 8.0 8.0
## 2126 6.0 8.0 6.0
## 2127 7.0 NA NA
## 2128 6.0 6.0 NA
## 2129 9.0 9.0 NA
## 2130 7.0 9.0 NA
## 2131 9.0 NA NA
## 2132 8.0 6.0 NA
## 2133 7.0 NA NA
## 2134 8.0 NA 7.0
## 2135 10.0 7.0 9.0
## 2136 8.0 9.0 NA
## 2137 9.0 7.0 8.0
## 2138 9.0 6.0 8.0
## 2139 10.0 8.0 7.0
## 2140 9.0 6.0 8.0
## 2141 9.0 7.0 7.0
## 2142 8.0 10.0 8.0
## 2143 9.0 6.0 9.0
## 2144 9.0 7.0 6.0
## 2145 9.0 8.0 8.0
## 2146 8.0 10.0 7.0
## 2147 10.0 5.0 9.0
## 2148 8.0 6.0 7.0
## 2149 8.0 8.0 10.0
## 2150 8.0 6.0 5.0
## 2151 7.0 7.0 8.0
## 2152 8.0 6.0 8.0
## 2153 8.0 4.0 NA
## 2154 7.0 5.0 NA
## 2155 9.0 9.0 NA
## 2156 7.0 6.0 NA
## 2157 7.0 7.0 NA
## 2158 8.0 9.0 NA
## 2159 8.0 8.0 8.0
## 2160 7.0 7.0 NA
## 2161 9.0 8.0 9.0
## 2162 8.0 8.0 NA
## 2163 NA 5.0 NA
## 2164 7.0 4.0 NA
## 2165 7.0 7.0 NA
## 2166 7.0 7.0 NA
## 2167 7.0 7.0 NA
## 2168 8.0 8.0 NA
## 2169 7.0 6.0 4.0
## 2170 6.0 4.0 5.0
## 2171 5.0 4.0 5.0
## 2172 6.0 4.0 5.0
## 2173 8.0 2.0 5.0
## 2174 5.0 8.0 2.0
## 2175 6.0 5.0 4.0
## 2176 6.0 5.0 4.0
## 2177 6.0 7.0 5.0
## 2178 5.0 4.0 3.0
## 2179 8.0 6.0 5.0
## 2180 6.0 5.0 6.0
## 2181 7.0 6.0 6.0
## 2182 5.0 4.0 4.0
## 2183 6.0 5.0 6.0
## 2184 6.0 6.0 5.0
## 2185 7.0 7.0 7.0
## 2186 5.0 6.0 6.0
## 2187 8.0 8.0 8.0
## 2188 7.0 7.0 7.0
## 2189 7.0 6.0 6.0
## 2190 8.0 7.0 5.0
## 2191 8.0 6.0 6.0
## 2192 6.0 6.0 6.0
## 2193 7.0 7.0 7.0
## 2194 7.0 7.0 8.0
## 2195 7.0 7.0 7.0
## 2196 7.0 6.0 6.0
## 2197 8.0 8.0 8.0
## 2198 7.0 7.0 7.0
## 2199 8.0 7.0 7.0
## 2200 8.0 8.0 8.0
## 2201 9.0 8.0 7.0
## 2202 9.0 8.0 8.0
## 2203 8.0 9.0 7.0
## 2204 9.0 8.0 9.0
## 2205 9.0 7.0 8.0
## 2206 8.0 9.0 8.0
## 2207 9.0 NA 9.0
## 2208 9.0 8.0 8.0
## 2209 9.0 7.0 9.0
## 2210 9.0 9.0 8.0
## 2211 8.0 7.0 8.0
## 2212 8.0 7.0 8.0
## 2213 9.0 9.0 9.0
## 2214 8.0 8.0 8.0
## 2215 10.0 8.0 10.0
## 2216 8.0 9.0 9.0
## 2217 7.0 7.0 5.0
## 2218 7.0 5.0 7.0
## 2220 6.0 6.0 6.0
## 2221 7.0 6.0 6.0
## 2222 7.0 6.0 6.0
## 2223 7.0 5.0 8.0
## 2224 7.0 6.0 6.0
## 2225 7.0 7.0 6.0
## 2226 7.0 8.0 7.0
## 2227 6.0 5.0 6.0
## 2228 7.0 6.0 5.0
## 2229 7.0 5.0 7.0
## 2230 7.0 6.0 6.0
## 2231 8.0 7.0 8.0
## 2232 6.0 5.0 7.0
## 2233 9.0 9.0 8.0
## 2234 9.0 6.0 7.0
## 2235 9.0 9.0 8.0
## 2236 9.0 5.0 8.0
## 2237 9.0 6.0 7.0
## 2238 7.0 9.0 7.0
## 2239 7.0 6.0 8.0
## 2240 8.0 6.0 9.0
## 2241 9.0 7.0 8.0
## 2242 8.0 8.0 7.0
## 2243 8.0 5.0 7.0
## 2244 8.0 6.0 7.0
## 2245 8.0 8.0 10.0
## 2246 8.0 8.0 9.0
## 2247 9.0 9.0 10.0
## 2248 9.0 9.0 10.0
## 2249 6.0 7.0 7.0
## 2250 9.0 2.0 8.0
## 2251 8.0 5.0 6.0
## 2252 9.0 2.0 4.0
## 2253 5.0 1.0 3.0
## 2254 NA 7.0 8.0
## 2255 6.0 9.0 8.0
## 2256 7.0 5.0 6.0
## 2257 8.0 4.0 6.0
## 2258 8.0 6.0 8.0
## 2259 8.0 4.0 6.0
## 2260 8.0 2.0 NA
## 2261 8.0 5.0 6.0
## 2262 6.0 4.0 5.0
## 2263 7.0 7.0 8.0
## 2264 7.0 8.0 9.0
## 2265 7.0 7.0 5.0
## 2266 5.0 4.0 5.0
## 2267 6.0 6.0 6.0
## 2268 6.0 5.0 5.0
## 2269 5.0 5.0 5.0
## 2270 5.0 6.0 6.0
## 2271 5.0 7.0 6.0
## 2272 8.0 8.0 7.0
## 2273 7.0 7.0 7.0
## 2274 7.0 8.0 7.0
## 2275 5.0 5.0 5.0
## 2276 5.0 5.0 5.0
## 2277 6.0 7.0 6.0
## 2278 7.0 8.0 7.0
## 2279 7.0 7.0 7.0
## 2280 8.0 8.0 8.0
## 2281 6.0 6.0 6.0
## 2282 5.0 5.0 5.0
## 2283 6.0 6.0 6.0
## 2284 5.0 5.0 5.0
## 2285 5.0 5.0 5.0
## 2286 5.0 5.0 5.0
## 2287 6.0 6.0 6.0
## 2288 5.0 7.0 7.0
## 2289 6.0 7.0 7.0
## 2290 6.0 7.0 7.0
## 2291 7.0 7.0 7.0
## 2292 7.0 5.0 7.0
## 2293 8.0 7.0 8.0
## 2294 7.0 7.0 6.0
## 2295 7.0 7.0 7.0
## 2296 7.0 6.0 6.0
## 2297 7.0 7.0 5.0
## 2298 8.0 8.0 8.0
## 2299 7.0 8.0 7.0
## 2300 7.0 6.0 7.0
## 2301 8.0 6.0 7.0
## 2302 6.0 6.0 7.0
## 2303 7.0 6.0 5.0
## 2304 5.0 5.0 5.0
## 2305 6.0 8.0 6.0
## 2306 7.0 5.0 5.0
## 2307 6.0 8.0 6.0
## 2308 7.0 8.0 7.0
## 2309 9.0 6.0 7.0
## 2310 8.0 6.0 7.0
## 2311 7.0 5.0 6.0
## 2312 3.0 3.0 5.0
## 2313 7.0 2.0 4.0
## 2314 7.0 5.0 NA
## 2315 6.0 3.0 4.0
## 2316 6.0 3.0 5.0
## 2317 7.0 8.0 8.0
## 2318 7.0 6.0 8.0
## 2319 8.0 8.0 8.0
## 2320 7.0 6.0 7.0
## 2321 7.0 5.0 NA
## 2322 7.0 6.0 NA
## 2323 7.0 5.0 NA
## 2325 7.0 5.0 NA
## 2326 7.0 5.0 NA
## 2327 7.0 7.0 5.0
## 2328 7.0 7.0 7.0
## 2329 7.0 5.0 NA
## 2330 8.0 NA NA
## 2331 6.0 4.0 6.0
## 2332 6.0 5.0 6.0
## 2333 7.0 6.0 6.0
## 2334 7.0 6.0 6.0
## 2335 7.0 6.0 7.0
## 2336 7.0 5.0 7.0
## 2337 7.0 7.0 7.0
## 2338 6.0 4.0 6.0
## 2339 6.0 6.0 5.0
## 2340 7.0 8.0 8.0
## 2341 7.0 8.0 7.0
## 2342 4.0 3.0 3.0
## 2343 7.0 5.0 5.0
## 2344 5.0 5.0 5.0
## 2345 8.0 7.0 8.0
## 2346 7.0 6.0 6.0
## 2347 7.0 8.0 7.0
## 2348 8.0 8.0 8.0
## 2349 8.0 9.0 8.0
## 2350 7.0 8.0 6.0
## 2351 8.0 9.0 10.0
## 2352 9.0 6.0 10.0
## 2353 9.0 7.0 9.0
## 2354 4.0 4.0 4.0
## 2355 4.0 4.0 4.0
## 2356 8.0 8.0 9.0
## 2357 6.0 10.0 5.0
## 2358 8.0 8.0 8.0
## 2359 4.0 9.0 5.0
## 2360 9.0 8.0 9.0
## 2361 8.0 8.0 9.0
## 2362 8.0 9.0 8.0
## 2363 10.0 9.0 10.0
## 2364 9.0 8.0 9.0
## 2365 8.0 9.0 10.0
## 2366 8.0 8.0 9.0
## 2367 9.0 9.0 9.0
## 2368 9.0 9.0 9.0
## 2369 9.0 9.0 9.0
## 2370 9.0 9.0 9.0
## 2371 9.0 7.0 6.0
## 2372 7.0 6.0 6.0
## 2373 8.0 7.0 6.0
## 2374 6.0 6.0 6.0
## 2375 7.0 7.0 7.0
## 2376 8.0 7.0 6.0
## 2377 7.0 9.0 9.0
## 2378 7.0 7.0 7.0
## 2379 8.0 8.0 8.0
## 2380 7.0 6.0 6.0
## 2381 10.0 8.0 6.0
## 2382 10.0 7.0 10.0
## 2383 10.0 7.0 10.0
## 2384 10.0 10.0 10.0
## 2385 10.0 8.0 9.0
## 2386 10.0 10.0 10.0
## 2387 8.0 8.0 9.0
## 2388 10.0 8.0 10.0
## 2389 10.0 8.0 9.0
## 2390 10.0 10.0 10.0
## 2391 7.0 6.0 5.0
## 2392 8.0 4.0 7.0
## 2393 8.0 4.0 7.0
## 2394 9.0 8.0 9.0
## 2395 7.0 6.0 8.0
## 2396 9.0 9.0 9.0
## 2397 8.0 8.0 8.0
## 2398 8.0 8.0 8.0
## 2399 9.0 9.0 9.0
## 2400 6.0 7.0 6.0
## 2401 6.0 NA 5.0
## 2402 6.0 6.0 5.0
## 2403 7.0 6.0 7.0
## 2404 7.0 8.0 5.0
## 2405 6.0 2.0 NA
## 2406 7.0 8.0 7.0
## 2407 NA NA NA
## 2408 8.0 NA 8.0
## 2409 6.0 5.0 5.0
## 2410 6.0 6.0 5.0
## 2411 NA NA NA
## 2412 10.0 5.0 NA
## 2414 NA NA NA
## 2416 7.0 8.0 NA
## 2417 7.0 7.0 NA
## 2419 NA NA NA
## 2420 NA 1.0 NA
## 2421 5.0 5.0 5.0
## 2422 6.0 5.0 5.0
## 2423 7.0 5.0 5.0
## 2424 5.0 5.0 5.0
## 2425 6.0 5.0 5.0
## 2426 6.0 6.0 5.0
## 2427 5.0 8.0 5.0
## 2428 5.0 6.0 2.0
## 2429 7.0 6.0 7.0
## 2430 5.0 5.0 5.0
## 2431 7.0 5.0 5.0
## 2432 5.0 5.0 4.0
## 2433 5.0 6.0 4.0
## 2434 6.0 5.0 9.0
## 2435 8.0 5.0 5.0
## 2436 8.0 8.0 8.0
## 2437 8.0 6.0 6.0
## 2438 7.0 6.0 7.0
## 2439 6.0 6.0 7.0
## 2440 7.0 7.0 7.0
## 2441 8.0 8.0 4.0
## 2442 8.0 9.0 8.0
## 2443 8.0 7.0 9.0
## 2444 5.0 8.0 5.0
## 2445 8.0 9.0 NA
## 2446 10.0 9.0 9.0
## 2447 7.0 7.0 NA
## 2448 NA 9.0 NA
## 2449 7.0 7.0 NA
## 2450 8.0 8.0 4.0
## 2451 7.0 6.0 6.0
## 2452 8.0 7.0 8.0
## 2453 7.0 7.0 8.0
## 2454 7.0 6.0 6.0
## 2455 7.0 7.0 5.0
## 2456 9.0 8.0 8.0
## 2457 7.0 7.0 8.0
## 2458 8.0 8.0 8.0
## 2459 7.0 7.0 8.0
## 2460 7.0 6.0 6.0
## 2461 8.0 4.0 6.0
## 2462 7.0 6.0 4.0
## 2463 9.0 3.0 9.0
## 2464 6.0 7.0 9.0
## 2465 6.0 6.0 6.0
## 2466 8.0 8.0 6.0
## 2467 8.0 8.0 6.0
## 2468 6.0 5.0 6.0
## 2469 7.0 6.0 7.0
## 2470 6.0 5.0 5.0
## 2471 7.0 5.0 6.0
## 2472 8.0 6.0 8.0
## 2473 9.0 3.0 9.0
## 2474 8.0 6.0 8.0
## 2475 9.0 6.0 10.0
## 2476 9.0 8.0 8.0
## 2477 8.0 6.0 7.0
## 2478 8.0 6.0 9.0
## 2479 7.0 6.0 8.0
## 2480 7.0 7.0 6.0
## 2481 10.0 8.0 10.0
## 2482 9.0 9.0 9.0
## 2483 8.0 6.0 8.0
## 2484 10.0 7.0 10.0
## 2485 10.0 7.0 10.0
## 2486 8.0 7.0 8.0
## 2487 10.0 9.0 10.0
## 2488 10.0 8.0 10.0
## 2489 9.0 6.0 8.0
## 2490 10.0 7.0 10.0
## 2491 10.0 8.0 9.0
## 2492 10.0 8.0 9.0
## 2493 8.0 8.0 8.0
## 2494 7.0 7.0 9.0
## 2495 10.0 6.0 10.0
## 2496 8.0 6.0 7.0
## 2497 9.0 8.0 8.0
## 2498 9.0 8.0 8.0
## 2499 9.0 6.0 7.0
## 2500 9.0 7.0 8.0
## 2501 10.0 8.0 9.0
## 2502 9.0 9.0 8.0
## 2503 7.0 6.0 8.0
## 2504 10.0 7.0 9.0
## 2505 10.0 5.0 9.0
## 2506 9.0 9.0 8.0
## 2507 9.0 8.0 9.0
## 2508 7.0 7.0 8.0
## 2509 10.0 5.0 6.0
## 2510 10.0 5.0 7.0
## 2511 10.0 6.0 7.0
## 2512 8.0 9.0 8.0
## 2513 9.0 8.0 9.0
## 2514 7.0 6.0 6.0
## 2515 10.0 5.0 5.0
## 2516 9.0 9.0 8.0
## 2517 6.0 6.0 6.0
## 2518 9.0 8.0 10.0
## 2519 7.0 4.0 6.0
## 2520 10.0 9.0 8.0
## 2521 7.0 9.0 8.0
## 2522 7.0 6.0 7.0
## 2523 5.0 5.0 NA
## 2524 7.0 6.0 7.0
## 2525 3.0 2.0 NA
## 2526 7.0 7.0 7.0
## 2527 3.0 3.0 3.0
## 2528 4.0 3.0 3.0
## 2529 4.0 2.0 1.0
## 2530 7.0 5.0 NA
## 2531 3.0 2.0 NA
## 2532 4.0 4.0 5.0
## 2533 5.0 5.0 5.0
## 2534 4.0 2.0 3.0
## 2535 3.0 2.0 NA
## 2536 5.0 3.0 6.0
## 2537 3.0 3.0 3.0
## 2538 7.0 8.0 NA
## 2539 4.0 3.0 3.0
## 2540 7.0 3.0 NA
## 2541 7.0 7.0 7.0
## 2542 8.0 8.0 8.0
## 2543 7.0 7.0 7.0
## 2544 9.0 7.0 9.0
## 2545 7.0 6.0 8.0
## 2546 8.0 6.0 7.0
## 2547 7.0 7.0 7.0
## 2548 7.0 7.0 7.0
## 2549 7.0 7.0 7.0
## 2550 7.0 7.0 7.0
## 2551 8.0 7.0 7.0
## 2552 7.0 7.0 7.0
## 2553 8.0 8.0 8.0
## 2554 7.0 7.0 7.0
## 2555 7.0 7.0 7.0
## 2556 7.0 7.0 7.0
## 2557 7.0 6.0 7.0
## 2558 10.0 10.0 10.0
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## 2808 8.0 2.0 3.0
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## 2810 8.0 1.0 NA
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## 3313 7.0 6.0 6.0
## 3314 8.0 4.0 3.0
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## 3360 7.0 6.0 9.0
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## 3366 6.0 5.0 8.0
## 3367 7.0 6.0 9.0
## 3368 7.0 9.0 9.0
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## 3386 8.0 7.0 7.0
## 3387 7.0 7.0 6.0
## 3388 8.0 7.0 7.0
## 3389 7.0 NA NA
## 3390 5.0 5.0 NA
## 3391 NA 2.0 NA
## 3392 7.0 4.0 NA
## 3393 7.0 NA NA
## 3394 6.0 5.0 NA
## 3395 7.0 7.0 5.0
## 3396 6.0 7.0 NA
## 3397 7.0 7.0 5.0
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## 3574 6.0 5.0 9.0
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## 3576 8.0 7.0 NA
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## 3583 7.0 3.0 6.0
## 3584 9.0 4.0 6.0
## 3585 8.0 10.0 NA
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## 3587 9.0 2.0 4.0
## 3588 9.0 9.0 8.0
## 3589 9.0 3.0 6.0
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## 3598 8.0 5.0 7.0
## 3599 8.0 8.0 6.0
## 3600 9.0 5.0 7.0
## 3601 9.0 8.0 NA
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## 3605 7.0 6.0 6.0
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## 4346 8.0 6.0 NA
## 4347 8.0 9.0 9.0
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## 4349 8.0 8.0 8.0
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## 4749 9.0 9.0 NA
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## 4888 5.0 3.0 NA
## 4889 6.0 5.0 7.0
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## 4891 6.0 4.0 5.0
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## 4894 7.0 7.0 7.0
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## 4897 8.0 6.0 NA
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## 4904 7.0 6.0 6.0
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## 4906 8.0 5.0 NA
## 4908 8.0 6.0 8.0
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## 4935 9.0 5.0 8.0
## 4936 9.0 9.0 7.0
## 4937 8.0 10.0 6.0
## 4938 10.0 8.0 8.0
## 4939 8.0 7.0 7.0
## 4940 7.0 7.0 7.0
## 4941 8.0 8.0 8.0
## 4942 9.0 6.0 10.0
## 4943 9.0 9.0 8.0
## 4944 9.0 8.0 8.0
## 4945 8.0 5.0 10.0
## 4946 10.0 6.0 10.0
## 4947 5.0 4.0 4.0
## 4948 8.0 7.0 6.0
## 4949 9.0 7.0 6.0
## 4950 9.0 7.0 7.0
## 4951 NA NA NA
## 4952 9.0 5.0 7.0
## 4955 9.0 10.0 9.0
## 4956 8.0 7.0 7.0
## 4957 9.0 9.0 8.0
## 4958 8.0 9.0 7.0
## 4959 8.0 7.0 6.0
## 4960 7.0 5.0 6.0
## 4961 5.0 5.0 5.0
## 4962 7.0 6.0 5.0
## 4963 7.0 5.0 6.0
## 4965 10.0 7.0 10.0
## 4966 7.0 6.0 7.0
## 4967 10.0 8.0 7.0
## 4968 9.0 9.0 8.0
## 4969 10.0 9.0 10.0
## 4970 10.0 8.0 8.0
## 4971 8.0 8.0 8.0
## 4972 8.0 8.0 8.0
## 4973 6.0 10.0 7.0
## 4974 10.0 9.0 10.0
## 4975 9.0 8.0 8.0
## 4976 10.0 8.0 8.0
## 4977 9.0 9.0 9.0
## 4978 10.0 8.0 8.0
## 4979 8.0 8.0 9.0
## 4980 10.0 5.0 9.0
## 4981 8.0 8.0 8.0
## 4982 9.0 8.0 8.0
## 4983 5.0 2.0 8.0
## 4984 9.0 7.0 8.0
## 4985 6.0 3.0 7.0
## 4986 8.0 7.0 8.0
## 4987 8.0 7.0 8.0
## 4988 8.0 8.0 NA
## 4989 7.0 8.0 5.0
## 4990 6.0 8.0 7.0
## 4992 7.0 6.0 6.0
## 4993 7.0 8.0 7.0
## 4994 8.0 7.0 6.0
## 4995 8.0 7.0 9.0
## 4996 8.0 5.0 7.0
## 4997 5.0 10.0 7.0
## 4998 9.0 2.0 10.0
## 4999 7.0 4.0 5.0
## 5000 6.0 6.0 5.0
## 5001 9.0 4.0 9.0
## 5002 8.0 6.0 6.0
## 5003 8.0 5.0 5.0
## 5004 6.0 6.0 5.0
## 5005 7.0 9.0 7.0
## 5006 9.0 7.0 7.0
## 5007 9.0 6.0 8.0
## 5008 9.0 8.0 6.0
## 5009 4.0 4.0 5.0
## 5010 8.0 8.0 8.0
## 5011 7.0 7.0 5.0
## 5012 7.0 6.0 6.0
## 5013 7.0 7.0 9.0
## 5014 7.0 4.0 8.0
## 5015 6.0 6.0 5.0
## 5016 8.0 7.0 9.0
## 5017 6.0 4.0 6.0
## 5018 7.0 8.0 7.0
## 5020 7.0 7.0 7.0
## 5021 7.0 7.0 7.0
## 5022 8.0 8.0 8.0
## 5023 6.0 6.0 7.0
## 5024 8.0 8.0 8.0
## 5025 7.0 7.0 7.0
## 5026 7.0 7.0 7.0
## 5027 8.0 7.0 7.0
## 5028 8.0 7.0 7.0
## 5029 3.0 2.0 3.0
## 5030 6.0 7.0 7.0
## 5031 9.0 9.0 9.0
## 5032 7.0 7.0 7.0
## 5033 8.0 8.0 8.0
## 5034 8.0 7.0 7.0
## 5035 7.0 5.0 7.0
## 5036 7.0 7.0 7.0
## 5037 7.0 6.0 6.0
## 5038 8.0 7.0 5.0
## 5039 8.0 7.0 7.0
## 5040 8.0 8.0 6.0
## 5041 7.0 7.0 7.0
## 5042 8.0 9.0 6.0
## 5043 7.0 7.0 6.0
## 5044 8.0 9.0 7.0
## 5045 7.0 9.0 7.0
## 5046 8.0 8.0 7.0
## 5047 8.0 7.0 8.0
## 5048 9.0 7.0 7.0
## 5049 7.0 7.0 7.0
## 5050 7.0 6.0 6.0
## 5051 8.0 8.0 8.0
## 5052 8.0 8.0 8.0
## 5053 6.0 5.0 5.0
## 5054 6.0 5.0 5.0
## 5055 5.0 4.0 5.0
## 5056 8.0 5.0 6.0
## 5057 8.0 7.0 6.0
## 5058 8.0 8.0 6.0
## 5059 7.0 6.0 6.0
## 5060 7.0 5.0 6.0
## 5063 8.0 7.0 7.0
## 5064 7.0 7.0 7.0
## 5065 7.0 7.0 7.0
## 5066 8.0 8.0 8.0
## 5067 8.0 7.0 7.0
## 5068 7.0 6.0 6.0
## 5069 6.0 5.0 5.0
## 5070 7.0 6.0 5.0
## 5071 7.0 4.0 6.0
## 5072 6.0 4.0 6.0
## 5073 4.0 6.0 6.0
## 5074 10.0 10.0 7.0
## 5075 4.0 3.0 5.0
## 5076 8.0 8.0 NA
## 5077 8.0 9.0 7.0
## 5078 10.0 10.0 9.0
## 5079 6.0 4.0 4.0
## 5080 9.0 9.0 10.0
## 5081 8.0 10.0 6.0
## 5082 9.0 9.0 9.0
## 5083 10.0 10.0 10.0
## 5084 7.0 7.0 6.0
## 5085 10.0 10.0 9.0
## 5087 6.0 6.0 6.0
## 5088 10.0 3.0 10.0
## 5089 10.0 4.0 4.0
## 5090 8.0 10.0 7.0
## 5091 7.0 9.0 8.0
## 5092 9.0 8.0 8.0
## 5093 7.0 7.0 8.0
## 5094 9.0 9.0 8.0
## 5095 9.0 9.0 9.0
## 5096 7.0 8.0 8.0
## 5097 8.0 6.0 7.0
## 5098 5.0 5.0 5.0
## 5101 8.0 8.0 8.0
## 5102 7.0 8.0 5.0
## 5103 8.0 7.0 8.0
## 5105 7.0 7.0 7.0
## 5106 9.0 9.0 8.0
## 5107 8.0 9.0 8.0
## 5108 6.0 5.0 6.0
## 5109 7.0 5.0 NA
## 5112 7.0 6.0 6.0
## 5113 7.0 7.0 6.0
## 5115 7.0 6.0 NA
## 5116 7.0 7.0 7.0
## 5119 7.0 5.0 6.0
## 5122 7.0 4.0 7.0
## 5123 6.0 5.0 NA
## 5124 7.0 7.0 8.0
## 5127 6.0 0.0 3.0
## 5128 7.0 4.0 5.0
## 5129 7.0 4.0 4.0
## 5130 6.0 7.0 6.0
## 5131 8.0 8.0 8.0
## 5132 10.0 7.0 7.0
## 5133 6.0 6.0 6.0
## 5134 8.0 9.0 NA
## 5136 8.0 8.0 8.0
## 5137 7.0 6.0 7.0
## 5138 7.0 5.0 5.0
## 5139 7.0 6.0 9.0
## 5140 8.0 6.0 8.0
## 5141 7.0 5.0 5.0
## 5142 9.0 7.0 9.0
## 5143 7.0 0.0 4.0
## 5144 6.0 2.0 2.0
## 5145 8.0 6.0 8.0
## 5146 7.0 7.0 7.0
## 5147 7.0 7.0 7.0
## 5148 7.0 7.0 7.0
## 5150 8.0 8.0 8.0
## 5151 8.0 7.0 7.0
## 5152 7.0 7.0 7.0
## 5153 7.0 8.0 7.0
## 5154 8.0 7.0 7.0
## 5155 7.0 7.0 7.0
## 5156 7.0 7.0 7.0
## 5157 8.0 7.0 7.0
## 5158 7.0 7.0 7.0
## 5159 8.0 8.0 8.0
## 5160 10.0 10.0 10.0
## 5161 8.0 7.0 7.0
## 5162 7.0 6.0 7.0
## 5163 8.0 6.0 NA
## 5164 8.0 NA 7.0
## 5165 5.0 6.0 6.0
## 5166 NA 4.0 4.0
## 5168 9.0 9.0 NA
## 5169 7.0 8.0 7.0
## 5171 NA 6.0 6.0
## 5172 NA 7.0 5.0
## 5173 7.0 NA NA
## 5174 6.0 6.0 NA
## 5175 NA NA NA
## 5176 5.0 5.0 6.0
## 5177 6.0 6.0 NA
## 5178 7.0 NA 5.0
## 5179 NA 4.0 NA
## 5180 7.0 NA 2.0
## 5181 5.0 2.0 6.0
## 5182 6.0 5.0 5.0
## 5183 5.0 5.0 6.0
## 5184 7.0 7.0 8.0
## 5185 7.0 6.0 7.0
## 5186 6.0 7.0 7.0
## 5187 6.0 7.0 7.0
## 5188 7.0 7.0 8.0
## 5189 7.0 7.0 6.0
## 5191 6.0 6.0 6.0
## 5192 6.0 4.0 4.0
## 5194 7.0 5.0 6.0
## 5195 6.0 6.0 5.0
## 5196 7.0 5.0 7.0
## 5197 5.0 5.0 5.0
## 5198 7.0 6.0 6.0
## 5199 9.0 7.0 7.0
## 5201 10.0 8.0 9.0
## 5202 10.0 9.0 10.0
## 5204 7.0 4.0 3.0
## 5205 8.0 8.0 9.0
## 5206 10.0 10.0 10.0
## 5207 10.0 9.0 8.0
## 5208 9.0 5.0 5.0
## 5209 6.0 6.0 6.0
## 5210 4.0 3.0 3.0
## 5211 9.0 10.0 9.0
## 5212 6.0 7.0 7.0
## 5213 9.0 8.0 9.0
## 5214 9.0 5.0 5.0
## 5215 9.0 5.0 10.0
## 5216 9.5 7.0 7.0
## 5217 9.0 8.0 9.0
## 5218 8.0 8.0 8.0
## 5219 7.0 7.0 7.0
## 5220 7.0 7.0 7.0
## 5221 8.0 8.0 8.0
## 5222 8.0 7.0 7.0
## 5224 7.0 6.0 6.0
## 5225 8.0 9.0 8.0
## 5226 8.0 7.0 7.0
## 5227 8.0 7.0 7.0
## 5228 7.0 6.0 6.0
## 5229 8.0 6.0 6.0
## 5230 8.0 8.0 8.0
## 5231 8.0 8.0 8.0
## 5233 7.0 7.0 7.0
## 5234 8.0 7.0 8.0
## 5235 8.0 7.0 7.0
## 5236 8.0 7.0 7.0
## 5237 7.0 6.0 5.0
## 5238 8.0 7.0 8.0
## 5239 7.0 6.0 8.0
## 5240 7.0 6.0 8.0
## 5241 7.0 7.0 6.0
## 5242 4.0 3.0 2.0
## 5243 9.0 7.0 7.0
## 5244 7.0 8.0 8.0
## 5245 8.0 8.0 7.0
## 5246 6.0 6.0 7.0
## 5247 7.0 7.0 7.0
## 5248 6.0 5.0 6.0
## 5249 8.0 8.0 8.0
## 5250 8.0 7.0 8.0
## 5252 7.0 7.0 7.0
## 5253 7.0 7.0 8.0
## 5254 8.0 6.0 8.0
## 5255 6.0 5.0 5.0
## 5256 8.0 9.0 8.0
## 5257 7.0 9.0 8.0
## 5258 8.0 6.0 8.0
## 5259 8.0 10.0 8.0
## 5260 9.0 9.0 9.0
## 5261 6.0 5.0 8.0
## 5262 8.0 10.0 8.0
## 5263 9.0 8.0 9.0
## 5264 9.0 8.0 9.0
## 5265 9.0 9.0 9.0
## 5266 9.0 8.0 8.0
## 5267 7.0 6.0 6.0
## 5268 7.0 9.0 8.0
## 5269 9.0 8.0 9.0
## 5270 9.0 4.0 9.0
## 5271 9.0 9.0 9.0
## 5272 9.0 5.0 10.0
## 5273 10.0 8.0 10.0
## 5274 8.0 6.0 7.0
## 5275 7.0 8.0 6.0
## 5276 6.0 7.0 6.0
## 5277 9.0 8.0 9.0
## 5278 7.0 7.0 7.0
## 5279 6.0 6.0 5.0
## 5280 8.0 4.0 3.0
## 5281 9.0 5.0 7.0
## 5282 6.0 5.0 6.0
## 5283 6.0 7.0 6.0
## 5284 6.0 5.0 5.0
## 5285 7.0 4.0 6.0
## 5286 7.0 5.0 7.0
## 5287 7.0 8.0 7.0
## 5288 6.0 8.0 5.0
## 5289 8.0 8.0 8.0
## 5290 8.0 9.0 7.0
## 5293 7.0 5.0 6.0
## 5294 8.0 8.0 7.0
## 5295 6.0 6.0 6.0
## 5296 6.0 7.0 5.0
## 5297 9.0 8.0 9.0
## 5298 6.0 5.0 5.0
## 5299 7.0 5.0 6.0
## 5300 6.0 6.0 5.0
## 5301 8.0 9.0 8.0
## 5302 7.0 8.0 7.0
## 5303 8.0 6.0 6.0
## 5304 7.0 6.0 5.0
## 5305 6.0 4.0 3.0
## 5306 7.0 9.0 8.0
## 5307 9.0 9.0 9.0
## 5309 8.0 8.0 8.0
## 5310 8.0 8.0 8.0
## 5311 8.0 8.0 6.0
## 5312 8.0 6.0 9.0
## 5313 7.0 7.0 6.0
## 5314 6.0 9.0 8.0
## 5315 6.0 4.0 6.0
## 5316 6.0 5.0 6.0
## 5317 7.0 8.0 6.0
## 5319 7.0 6.0 6.0
## 5320 7.0 7.0 7.0
## 5321 6.0 7.0 6.0
## 5322 5.0 4.0 4.0
## 5323 4.0 3.0 5.0
## shared_interests_partner like guess_prob_liked met decision
## 1 5.0 7.0 6.0 0 1
## 2 6.0 7.0 5.0 1 1
## 3 7.0 7.0 NA 1 1
## 4 8.0 7.0 6.0 0 1
## 5 6.0 6.0 6.0 0 1
## 6 4.0 6.0 5.0 0 0
## 7 7.0 6.0 5.0 0 1
## 8 6.0 6.0 7.0 NA 0
## 9 8.0 7.0 7.0 0 1
## 10 8.0 6.0 6.0 0 1
## 11 3.0 6.0 4.0 0 0
## 12 6.0 7.0 3.0 0 0
## 13 4.0 6.0 7.0 0 0
## 14 7.0 7.0 8.0 0 1
## 15 8.0 8.0 6.0 1 0
## 16 2.0 6.0 5.0 0 0
## 17 9.0 8.0 7.0 0 1
## 18 5.0 5.0 6.0 0 0
## 19 5.0 5.0 6.0 0 1
## 20 8.0 8.0 7.0 0 1
## 21 9.0 8.0 7.0 1 0
## 22 7.0 8.0 7.0 0 0
## 23 7.0 8.0 7.0 0 0
## 24 7.0 8.0 7.0 0 0
## 25 10.0 9.0 NA 0 0
## 26 9.0 8.0 NA 0 0
## 27 9.0 8.0 7.0 0 0
## 28 7.0 8.0 7.0 0 0
## 29 9.0 9.0 7.0 0 0
## 30 7.0 8.0 7.0 0 0
## 31 7.0 6.0 7.0 0 0
## 32 8.0 8.0 1.0 1 0
## 33 7.0 4.0 1.0 0 0
## 34 10.0 8.0 10.0 1 1
## 35 9.0 7.0 7.0 0 0
## 36 2.0 4.0 3.0 0 0
## 37 5.0 5.0 1.0 0 0
## 38 7.0 6.0 6.0 0 0
## 39 8.0 8.0 8.0 0 1
## 40 8.0 10.0 8.0 0 1
## 41 2.0 7.0 5.0 0 0
## 42 5.0 6.0 4.0 1 0
## 43 5.0 6.0 5.0 1 0
## 44 9.0 8.0 2.0 1 1
## 45 8.0 7.0 4.0 0 0
## 46 8.0 7.0 3.0 0 1
## 47 6.0 6.0 3.0 0 1
## 48 5.0 6.0 4.0 0 1
## 49 9.0 9.0 4.0 0 1
## 50 5.0 10.0 3.0 0 1
## 51 6.0 6.0 6.0 0 1
## 52 2.0 7.0 6.0 0 1
## 53 2.0 6.0 2.0 1 0
## 54 8.0 9.0 4.0 0 1
## 55 2.0 4.0 3.0 1 0
## 56 1.0 2.0 2.0 0 0
## 57 4.0 8.0 7.0 0 1
## 58 3.0 5.0 5.0 0 0
## 59 9.0 9.0 6.0 0 1
## 60 7.0 9.0 8.0 0 1
## 61 4.0 7.0 4.0 0 0
## 62 4.0 7.0 3.0 1 0
## 63 8.0 7.0 8.0 1 0
## 64 6.0 8.0 6.0 1 1
## 65 5.0 5.0 6.0 1 0
## 66 4.0 5.0 5.0 0 0
## 67 7.0 8.0 7.0 0 1
## 68 5.0 5.0 7.0 0 0
## 69 6.0 6.0 6.0 0 1
## 70 6.0 7.0 5.0 0 1
## 71 5.0 4.0 5.0 0 1
## 72 7.0 8.0 7.0 0 1
## 73 7.0 5.0 5.0 0 1
## 74 5.0 5.0 6.0 0 1
## 75 7.0 6.0 5.0 0 1
## 76 7.0 6.0 5.0 0 1
## 77 7.0 6.0 6.0 0 1
## 78 6.0 5.0 4.0 0 1
## 79 9.0 7.0 10.0 0 1
## 80 8.0 8.0 7.0 0 1
## 81 6.0 5.0 5.0 0 1
## 82 10.0 10.0 5.0 0 1
## 83 6.0 8.0 5.0 0 1
## 84 7.0 7.0 5.0 0 1
## 85 5.0 5.0 5.0 0 1
## 86 5.0 6.0 5.0 0 1
## 87 6.0 7.0 5.0 0 1
## 88 6.0 7.0 5.0 0 1
## 89 5.0 7.0 6.0 0 1
## 90 8.0 8.0 5.0 0 1
## 91 NA 6.0 3.0 0 0
## 92 NA 1.0 1.0 1 0
## 93 NA 7.0 6.0 0 1
## 94 NA 10.0 1.0 1 0
## 95 NA 6.0 1.0 0 0
## 96 NA 6.0 1.0 0 0
## 97 NA 7.0 1.0 0 1
## 98 NA 5.0 5.0 0 0
## 99 9.0 9.0 9.0 0 1
## 100 8.0 9.0 10.0 0 1
## 101 6.0 7.0 4.0 0 0
## 102 4.0 7.0 2.0 0 0
## 103 4.0 5.0 2.0 1 0
## 104 5.0 6.0 3.0 0 0
## 105 6.0 6.0 5.0 0 0
## 106 4.0 7.0 4.0 0 0
## 107 7.0 7.0 4.0 0 0
## 108 5.0 7.0 4.0 0 0
## 109 5.0 6.0 4.0 0 0
## 110 5.0 6.0 3.0 0 0
## 111 5.0 8.0 4.0 0 0
## 112 5.0 8.0 4.0 0 0
## 113 5.0 7.0 3.0 0 0
## 114 5.0 6.0 3.0 1 0
## 115 5.0 7.0 4.0 1 0
## 116 6.0 7.0 4.0 0 0
## 117 5.0 8.0 4.0 1 1
## 118 5.0 9.0 4.0 0 1
## 119 5.0 8.0 4.0 0 1
## 120 1.0 1.0 1.0 0 0
## 121 10.0 10.0 10.0 1 1
## 122 10.0 10.0 10.0 0 1
## 123 10.0 10.0 10.0 0 1
## 124 10.0 10.0 10.0 NA 1
## 125 10.0 10.0 10.0 1 1
## 126 10.0 10.0 10.0 1 1
## 127 10.0 10.0 10.0 NA 1
## 128 10.0 10.0 10.0 0 1
## 129 10.0 10.0 10.0 0 1
## 130 10.0 10.0 10.0 0 1
## 131 8.0 7.0 7.0 0 1
## 132 9.0 9.0 7.0 0 1
## 133 8.0 7.0 7.0 0 1
## 134 9.0 8.0 10.0 1 1
## 135 10.0 7.0 7.0 1 1
## 136 8.0 7.0 7.0 0 1
## 137 9.0 8.0 7.0 1 1
## 138 7.0 8.0 7.0 0 1
## 139 7.0 7.0 7.0 0 1
## 140 8.0 7.0 7.0 1 1
## 141 7.0 8.0 6.0 0 1
## 142 8.0 10.0 1.0 1 1
## 143 NA 6.0 1.0 0 1
## 144 9.0 9.0 7.0 0 1
## 145 5.0 5.0 7.0 0 0
## 146 6.0 9.0 2.0 1 1
## 147 5.0 8.0 4.0 0 1
## 148 7.0 9.0 7.0 0 1
## 149 7.0 7.0 8.0 0 1
## 150 NA 6.0 6.0 0 1
## 151 7.0 7.0 6.0 0 1
## 152 7.0 7.0 5.0 0 1
## 153 6.0 6.0 7.0 0 1
## 154 7.0 6.0 6.0 0 1
## 155 7.0 6.0 7.0 0 0
## 156 7.0 6.0 6.0 0 1
## 157 6.0 7.0 7.0 0 1
## 158 7.0 7.0 6.0 0 1
## 159 7.0 6.0 5.0 0 1
## 160 7.0 6.0 6.0 0 0
## 161 7.0 2.0 1.0 0 0
## 162 3.0 4.0 3.0 0 0
## 163 4.0 2.0 2.0 0 0
## 164 5.0 6.0 4.0 0 1
## 165 3.0 3.0 3.0 0 0
## 166 4.0 3.0 3.0 0 0
## 167 3.0 5.0 4.0 0 0
## 168 4.0 4.0 4.0 0 0
## 169 5.0 8.0 4.0 0 1
## 170 4.0 3.0 6.0 0 0
## 171 6.0 7.0 5.0 0 0
## 172 5.0 7.0 6.0 0 1
## 173 4.0 5.0 1.0 0 0
## 174 5.0 7.0 7.0 0 0
## 175 5.0 6.0 5.0 0 0
## 176 5.0 7.0 6.0 0 0
## 177 5.0 7.0 5.0 0 1
## 178 5.0 7.0 5.0 0 1
## 179 5.0 7.0 5.0 0 0
## 180 5.0 7.0 6.0 0 0
## 181 9.0 6.5 8.0 0 1
## 182 7.0 8.0 8.0 0 1
## 183 7.0 7.0 7.0 0 1
## 184 10.0 8.0 8.0 0 1
## 185 6.0 7.0 7.0 0 1
## 186 6.0 6.0 6.0 0 1
## 187 10.0 9.0 9.0 0 1
## 188 10.0 8.0 8.0 0 1
## 189 6.0 6.0 6.0 0 1
## 190 6.0 7.0 8.0 0 1
## 191 6.0 6.0 6.0 0 0
## 192 7.0 6.0 9.0 0 0
## 193 6.0 5.0 5.0 0 0
## 194 7.0 7.0 7.0 0 0
## 195 5.0 4.0 10.0 0 0
## 196 7.0 6.0 7.0 0 0
## 197 7.0 7.0 7.0 0 0
## 198 8.0 8.0 8.0 0 1
## 199 6.0 6.0 6.0 0 0
## 200 6.0 7.0 7.0 0 0
## 201 7.0 9.0 1.0 1 0
## 202 5.0 7.0 2.0 0 0
## 203 6.0 9.0 3.0 NA 1
## 204 3.0 6.0 1.0 0 0
## 205 2.0 6.0 1.0 0 0
## 206 4.0 5.0 2.0 0 0
## 207 5.0 8.0 2.0 0 0
## 208 3.0 5.0 1.0 0 0
## 209 3.0 6.0 1.0 0 0
## 210 8.0 9.0 5.0 0 1
## 211 6.0 6.0 1.0 0 0
## 212 2.0 2.0 1.0 0 0
## 213 2.0 6.0 1.0 0 0
## 214 2.0 6.0 1.0 0 0
## 215 2.0 6.0 1.0 0 0
## 216 5.0 8.0 3.0 0 0
## 217 5.0 6.0 4.0 0 0
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## 1812 7.0 7.0 6.0 1 1
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## 1818 8.0 8.0 5.0 0 0
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## 1830 5.0 8.0 5.0 0 0
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## 1833 NA 4.0 3.0 0 0
## 1834 NA 7.0 5.0 0 0
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## 1837 NA 5.0 2.0 0 0
## 1838 NA 6.0 3.0 0 0
## 1839 8.0 10.0 10.0 1 1
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## 1842 NA 6.0 3.0 0 0
## 1843 NA 6.0 3.0 0 0
## 1844 NA 7.0 4.0 0 0
## 1845 NA 6.0 3.0 0 0
## 1846 NA 7.0 6.0 0 0
## 1847 NA 6.0 4.0 0 0
## 1848 NA 5.0 3.0 0 0
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## 1856 5.0 6.0 6.0 0 1
## 1857 7.0 7.0 9.0 1 1
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## 1859 3.0 4.0 6.0 0 0
## 1860 5.0 7.0 6.0 0 1
## 1861 4.0 5.0 5.0 0 0
## 1862 3.0 5.0 5.0 0 0
## 1863 8.0 10.0 10.0 1 1
## 1864 5.0 6.0 6.0 0 0
## 1865 1.0 1.0 1.0 0 0
## 1866 1.0 2.0 1.0 0 0
## 1867 6.0 5.0 1.0 1 0
## 1868 1.0 4.0 1.0 0 0
## 1869 7.0 8.0 5.0 0 0
## 1870 7.0 8.0 5.0 0 1
## 1871 2.0 4.0 2.0 0 0
## 1872 2.0 1.0 1.0 0 0
## 1873 2.0 7.0 2.0 1 0
## 1874 2.0 6.0 1.0 0 0
## 1875 2.0 5.0 1.0 0 0
## 1876 2.0 6.0 3.0 0 1
## 1877 1.0 4.0 1.0 0 0
## 1878 5.0 9.0 3.0 0 0
## 1879 7.0 9.0 5.0 1 0
## 1880 2.0 7.0 2.0 0 0
## 1881 NA 5.0 6.0 0 0
## 1882 7.0 4.0 8.0 0 0
## 1883 6.0 8.0 6.0 0 1
## 1884 8.0 7.0 3.0 0 0
## 1885 NA 6.0 8.0 0 0
## 1886 4.0 5.0 7.0 0 0
## 1887 3.0 3.0 8.0 0 0
## 1888 NA NA NA 0 0
## 1889 8.0 7.0 7.0 0 1
## 1890 8.0 5.0 7.0 0 0
## 1891 NA 4.0 7.0 0 0
## 1892 8.0 7.0 7.0 0 1
## 1893 NA 4.0 8.0 0 0
## 1894 NA 7.0 6.0 0 1
## 1895 6.0 7.0 NA 0 1
## 1896 NA 6.0 5.0 0 1
## 1897 NA 5.0 4.0 0 0
## 1898 NA 7.0 NA 0 0
## 1899 2.0 7.0 1.0 0 0
## 1900 7.0 7.0 2.0 0 0
## 1901 5.0 7.0 4.0 0 0
## 1902 NA 7.0 4.0 0 0
## 1903 7.0 7.0 5.0 0 0
## 1904 NA 7.0 4.0 0 1
## 1905 NA 5.0 4.0 0 0
## 1906 8.0 7.0 5.0 0 1
## 1907 NA 8.0 6.0 0 0
## 1908 7.0 9.0 5.0 0 1
## 1909 3.0 8.0 5.0 0 0
## 1910 NA 7.0 4.0 0 0
## 1911 NA 7.0 4.0 0 0
## 1912 NA 8.0 5.0 0 0
## 1913 1.0 2.0 3.0 0 0
## 1914 5.0 6.0 4.0 0 0
## 1915 6.0 6.0 7.0 0 0
## 1916 7.0 9.0 8.0 0 0
## 1917 9.0 8.0 8.0 0 1
## 1918 7.0 7.5 6.0 0 0
## 1919 8.0 7.0 7.0 0 0
## 1920 5.0 4.0 4.0 0 0
## 1921 6.0 5.0 6.0 0 0
## 1922 7.0 3.0 3.0 0 0
## 1923 4.0 4.0 5.0 0 0
## 1924 NA 7.0 7.0 0 0
## 1925 2.0 4.0 6.0 0 0
## 1926 NA 5.0 6.0 0 0
## 1927 8.0 7.0 7.0 0 0
## 1928 10.0 9.0 9.0 0 1
## 1929 6.0 6.0 6.0 0 0
## 1930 NA 5.0 6.0 0 0
## 1931 4.0 6.0 5.0 0 0
## 1932 9.0 8.0 7.0 0 1
## 1933 8.0 7.0 5.0 0 0
## 1934 8.0 NA NA NA 0
## 1935 9.0 7.0 7.0 0 1
## 1936 9.0 9.0 6.0 0 1
## 1937 7.0 5.0 4.0 0 0
## 1938 7.0 NA 8.0 0 1
## 1939 5.0 6.0 7.0 0 0
## 1940 6.0 8.0 7.0 0 1
## 1941 NA 6.0 5.0 0 0
## 1942 8.0 7.0 5.0 0 0
## 1943 NA 7.0 8.0 NA 0
## 1944 7.0 7.0 7.0 0 0
## 1945 NA 5.0 5.0 0 0
## 1946 5.0 5.0 5.0 0 1
## 1947 NA 5.0 5.0 0 0
## 1948 6.0 5.0 5.0 0 1
## 1949 6.0 5.0 5.0 0 1
## 1950 7.0 8.0 7.0 0 1
## 1951 6.0 7.0 6.0 0 1
## 1952 6.0 5.0 5.0 0 0
## 1953 NA 4.0 4.0 0 0
## 1954 5.0 5.0 5.0 0 1
## 1955 NA 6.0 7.0 0 1
## 1956 6.0 7.0 6.0 0 1
## 1957 4.0 4.0 4.0 0 0
## 1958 NA 5.0 5.0 0 1
## 1959 NA 6.0 5.0 0 1
## 1960 NA 5.0 5.0 0 0
## 1961 7.0 5.0 5.0 0 0
## 1962 5.0 7.0 5.0 0 0
## 1963 9.0 8.0 5.0 1 0
## 1964 6.0 8.0 5.0 0 1
## 1965 2.0 5.0 3.0 0 0
## 1966 6.0 8.0 3.0 0 1
## 1967 5.0 7.0 5.0 0 0
## 1968 4.0 5.0 3.0 0 0
## 1969 7.0 8.0 5.0 1 1
## 1970 8.0 8.0 3.0 0 0
## 1971 5.0 5.0 5.0 0 0
## 1972 9.0 8.0 6.0 0 1
## 1973 6.0 6.0 5.0 0 0
## 1974 8.0 8.0 5.0 0 1
## 1975 8.0 8.0 3.0 1 1
## 1976 7.0 8.0 5.0 0 1
## 1977 2.0 2.0 5.0 0 0
## 1978 2.0 1.0 3.0 0 0
## 1979 6.0 6.0 5.0 0 0
## 1980 5.0 7.0 5.0 0 1
## 1981 3.0 3.0 3.0 0 0
## 1982 7.0 6.0 6.0 0 0
## 1983 8.0 8.0 8.0 0 0
## 1984 5.0 5.0 5.0 0 0
## 1985 5.0 5.0 5.0 0 0
## 1986 3.0 4.0 4.0 0 0
## 1987 3.0 4.0 4.0 0 0
## 1988 6.0 8.0 5.0 0 1
## 1989 6.0 8.0 5.0 0 0
## 1990 5.0 5.0 7.0 0 0
## 1991 7.0 6.0 6.0 0 0
## 1992 6.0 5.0 7.0 0 0
## 1993 3.0 3.0 3.0 0 0
## 1994 3.0 3.0 3.0 0 0
## 1995 10.0 8.0 10.0 1 1
## 1996 5.0 8.0 7.0 0 1
## 1997 10.0 4.0 1.0 0 0
## 1998 8.0 7.0 6.0 0 1
## 1999 6.0 6.0 6.0 0 0
## 2000 6.0 5.0 5.0 0 0
## 2001 6.0 7.0 6.0 0 0
## 2002 4.0 6.0 5.0 0 0
## 2003 5.0 7.0 7.0 0 0
## 2004 10.0 9.0 9.0 0 1
## 2005 8.0 3.0 2.0 0 0
## 2006 2.0 2.0 2.0 0 0
## 2007 7.0 8.0 8.0 0 1
## 2008 7.0 8.0 7.0 0 1
## 2009 1.0 1.0 3.0 0 0
## 2010 4.0 3.0 5.0 0 0
## 2011 8.0 7.0 8.0 1 0
## 2012 7.0 6.0 6.0 0 1
## 2013 2.0 1.0 4.0 0 0
## 2014 4.0 3.0 5.0 0 0
## 2015 6.0 6.0 6.0 0 0
## 2016 4.0 4.0 5.0 0 0
## 2017 6.0 7.0 7.0 0 0
## 2018 4.0 3.0 5.0 0 0
## 2019 7.0 2.0 6.0 0 0
## 2020 8.0 7.0 6.0 0 1
## 2021 3.0 3.0 4.0 0 0
## 2022 5.0 6.0 6.0 0 0
## 2023 7.0 6.0 7.0 0 0
## 2024 6.0 3.0 7.0 0 0
## 2025 5.0 9.0 5.0 0 1
## 2026 7.0 7.0 5.0 0 1
## 2027 8.0 9.0 7.0 0 1
## 2028 8.0 9.0 6.0 0 1
## 2029 8.0 9.0 5.0 0 1
## 2030 6.0 8.0 4.0 0 1
## 2031 7.0 8.0 5.0 0 1
## 2032 6.0 8.0 5.0 0 1
## 2033 7.0 9.0 5.0 0 1
## 2034 6.0 9.0 4.0 0 1
## 2035 6.0 8.0 4.0 0 1
## 2036 6.0 7.0 5.0 0 1
## 2037 6.0 8.0 4.0 0 1
## 2038 7.0 9.0 6.0 0 1
## 2039 6.0 8.0 5.0 0 1
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## 2043 9.0 9.0 7.0 0 1
## 2044 9.0 8.0 6.0 0 1
## 2045 NA 5.0 3.0 0 0
## 2046 3.0 5.0 4.0 0 0
## 2047 NA 6.0 4.0 0 0
## 2048 6.0 7.0 6.0 0 1
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## 2052 5.0 7.0 8.0 0 1
## 2053 6.0 7.0 6.0 0 1
## 2054 NA 7.0 4.0 0 1
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## 5171 3.0 5.0 4.0 NA 1
## 5172 4.0 5.0 4.0 0 1
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## 5174 NA 4.0 4.0 0 0
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## 5183 3.0 4.0 1.0 0 0
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## 5197 5.0 7.0 6.0 0 1
## 5198 5.0 7.0 5.0 0 1
## 5199 1.0 7.0 5.0 0 0
## 5201 8.0 10.0 10.0 NA 1
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## 5210 3.0 3.0 1.0 0 0
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## 5212 4.0 7.0 6.0 NA 0
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## 5225 8.0 8.0 NA NA 1
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## 5230 8.0 8.0 NA NA 1
## 5231 8.0 8.0 NA NA 1
## 5233 7.0 7.0 8.0 0 0
## 5234 8.0 8.0 8.0 0 1
## 5235 7.0 7.0 8.0 NA 1
## 5236 7.0 7.0 8.0 NA 0
## 5237 5.0 7.0 6.0 0 1
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## 5239 4.0 5.0 4.0 0 0
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## 5267 4.0 5.0 5.0 0 0
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## 5270 6.0 7.0 3.0 0 0
## 5271 8.0 9.0 7.0 0 1
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## 5273 8.0 9.0 6.0 0 1
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## 5275 8.0 8.0 5.0 0 1
## 5276 4.0 5.0 4.0 0 0
## 5277 7.0 8.0 7.0 0 1
## 5278 6.0 7.0 5.0 0 1
## 5279 2.0 5.0 5.0 0 0
## 5280 3.0 3.0 3.0 0 0
## 5281 2.0 6.0 6.0 0 0
## 5282 4.0 6.0 3.0 0 0
## 5283 8.0 8.0 7.0 0 1
## 5284 4.0 5.0 5.0 0 0
## 5285 4.0 4.0 4.0 0 0
## 5286 4.0 4.0 4.0 0 0
## 5287 6.0 7.0 7.0 0 1
## 5288 8.0 8.0 7.0 0 1
## 5289 9.0 9.0 8.0 0 1
## 5290 6.0 7.0 7.0 0 1
## 5293 5.0 6.0 5.0 0 0
## 5294 9.0 9.0 8.0 0 1
## 5295 6.0 7.0 6.0 0 0
## 5296 6.0 6.0 6.0 0 0
## 5297 8.0 8.0 8.0 0 1
## 5298 5.0 7.0 7.0 0 1
## 5299 4.0 6.0 5.0 0 0
## 5300 4.0 5.0 4.0 0 0
## 5301 9.0 9.0 8.0 0 1
## 5302 8.0 8.0 6.0 0 1
## 5303 7.0 7.0 5.0 0 0
## 5304 5.0 6.0 6.0 0 0
## 5305 2.0 4.0 4.0 0 0
## 5306 7.0 8.0 7.0 0 1
## 5307 8.0 9.0 8.0 0 1
## 5309 8.0 8.0 7.0 0 1
## 5310 7.0 8.0 7.0 1 1
## 5311 7.0 7.0 7.0 0 1
## 5312 6.0 6.0 5.0 0 0
## 5313 7.0 7.0 7.0 0 0
## 5314 7.0 8.0 8.0 0 1
## 5315 9.0 4.0 6.0 0 0
## 5316 7.0 5.0 4.0 0 0
## 5317 5.0 7.0 7.0 0 0
## 5319 6.0 6.0 5.0 0 0
## 5320 8.0 8.0 6.0 0 1
## 5321 6.0 7.0 6.0 0 1
## 5322 4.0 5.0 4.0 0 0
## 5323 4.0 4.0 3.0 0 0
## [ reached 'max' / getOption("max.print") -- omitted 2657 rows ]partially_filtered_dates[, columns_of_interest] <- lapply(partially_filtered_dates[, columns_of_interest, drop = FALSE], function(x) {
median_value <- median(x, na.rm = TRUE)
x[is.na(x)] <- median_value
return(x)
})
filtered_dates <- partially_filtered_dates
filtered_dates## date_id participant_id partner_id attractive_o sincere_o intelligence_o
## 1 100000 10000 10010 6.0 8.0 8.0
## 2 100001 10000 10011 7.0 8.0 10.0
## 3 100002 10000 10012 10.0 10.0 10.0
## 4 100003 10000 10013 7.0 8.0 9.0
## 5 100004 10000 10014 8.0 7.0 9.0
## 6 100005 10000 10015 7.0 7.0 8.0
## 7 100006 10000 10016 3.0 6.0 7.0
## 8 100007 10000 10017 6.0 7.0 5.0
## 9 100008 10000 10018 7.0 7.0 8.0
## 10 100009 10000 10019 6.0 6.0 6.0
## 11 100010 10001 10010 8.0 7.0 6.0
## 12 100011 10001 10011 7.0 6.0 10.0
## 13 100012 10001 10012 10.0 10.0 10.0
## 14 100013 10001 10013 9.0 9.0 9.0
## 15 100014 10001 10014 10.0 10.0 10.0
## 16 100015 10001 10015 7.0 8.0 7.0
## 17 100016 10001 10016 5.0 3.0 4.0
## 18 100017 10001 10017 7.0 7.0 7.0
## 19 100018 10001 10018 8.0 6.0 9.0
## 20 100019 10001 10019 6.0 5.0 7.0
## 21 100020 10002 10010 7.0 8.0 6.0
## 22 100021 10002 10011 6.0 7.0 10.0
## 23 100022 10002 10012 10.0 10.0 10.0
## 24 100023 10002 10013 7.0 9.0 8.0
## 25 100024 10002 10014 6.0 10.0 8.0
## 26 100025 10002 10015 7.0 6.0 6.0
## 27 100026 10002 10016 6.0 3.0 5.0
## 28 100027 10002 10017 4.0 5.0 6.0
## 29 100028 10002 10018 7.0 7.0 6.0
## 30 100029 10002 10019 5.0 6.0 8.0
## 31 100030 10003 10010 6.0 7.0 8.0
## 32 100031 10003 10011 6.0 5.0 10.0
## 33 100032 10003 10012 10.0 10.0 10.0
## 34 100033 10003 10013 7.0 7.0 7.0
## 35 100034 10003 10014 8.0 8.0 9.0
## 36 100035 10003 10015 6.0 6.0 7.0
## 37 100036 10003 10016 7.0 6.0 3.0
## 38 100037 10003 10017 6.0 7.0 8.0
## 39 100038 10003 10018 7.0 7.0 7.0
## 40 100039 10003 10019 7.0 8.0 8.0
## 41 100040 10004 10010 6.0 8.0 8.0
## 42 100041 10004 10011 6.0 7.0 10.0
## 43 100042 10004 10012 10.0 10.0 10.0
## 44 100043 10004 10013 6.0 8.0 6.0
## 45 100044 10004 10014 5.0 9.0 9.0
## 46 100045 10004 10015 5.0 8.0 7.0
## 47 100046 10004 10016 2.0 3.0 4.0
## 48 100047 10004 10017 5.0 5.0 6.0
## 49 100048 10004 10018 5.0 9.0 9.0
## 50 100049 10004 10019 3.0 10.0 7.0
## 51 100050 10005 10010 7.0 9.0 8.0
## 52 100051 10005 10011 6.0 8.0 10.0
## 53 100052 10005 10012 10.0 10.0 10.0
## 54 100053 10005 10013 6.0 8.0 8.0
## 55 100054 10005 10014 9.0 7.0 9.0
## 56 100055 10005 10015 6.0 6.0 8.0
## 57 100056 10005 10016 5.0 6.0 8.0
## 58 100057 10005 10017 6.0 7.0 8.0
## 59 100058 10005 10018 6.0 7.0 7.0
## 60 100059 10005 10019 7.0 10.0 10.0
## 61 100060 10006 10010 7.0 8.0 8.0
## 62 100061 10006 10011 7.0 9.0 10.0
## 63 100062 10006 10012 10.0 10.0 10.0
## 64 100063 10006 10013 8.0 8.0 9.0
## 65 100064 10006 10014 8.0 7.0 9.0
## 66 100065 10006 10015 8.0 8.0 8.0
## 67 100066 10006 10016 7.0 6.0 5.0
## 68 100067 10006 10017 8.0 7.0 6.0
## 69 100068 10006 10018 9.0 7.0 9.0
## 70 100069 10006 10019 7.0 6.0 8.0
## 71 100070 10007 10010 8.0 7.0 6.0
## 72 100071 10007 10011 8.0 8.0 10.0
## 73 100072 10007 10012 10.0 10.0 10.0
## 74 100073 10007 10013 8.0 8.0 8.0
## 75 100074 10007 10014 9.0 9.0 8.0
## 76 100075 10007 10015 8.0 7.0 7.0
## 77 100076 10007 10016 6.0 4.0 7.0
## 78 100077 10007 10017 9.0 8.0 8.0
## 79 100078 10007 10018 8.0 7.0 8.0
## 80 100079 10007 10019 8.0 10.0 9.0
## 81 100080 10008 10010 6.0 8.0 7.0
## 82 100081 10008 10011 8.0 8.0 10.0
## 83 100082 10008 10012 10.0 10.0 10.0
## 84 100083 10008 10013 7.0 8.0 7.0
## 85 100084 10008 10014 7.0 9.0 8.0
## 86 100085 10008 10015 6.0 6.0 7.0
## 87 100086 10008 10016 8.0 6.0 7.0
## 88 100087 10008 10017 7.0 7.0 7.0
## 89 100088 10008 10018 6.0 6.0 6.0
## 90 100089 10008 10019 5.0 6.0 6.0
## 91 100090 10009 10010 7.0 8.0 7.0
## 92 100091 10009 10011 1.0 1.0 1.0
## 93 100092 10009 10012 10.0 10.0 10.0
## 94 100093 10009 10013 7.0 8.0 7.0
## 95 100094 10009 10014 6.0 9.0 9.0
## 96 100095 10009 10015 6.0 8.0 7.0
## 97 100096 10009 10016 7.0 7.0 4.0
## 98 100097 10009 10017 6.0 6.0 6.0
## 99 100098 10009 10018 7.0 7.0 7.0
## 100 100099 10009 10019 6.0 6.0 6.0
## 101 100100 10010 10000 6.0 9.0 7.0
## 102 100101 10010 10001 5.0 7.0 8.0
## 103 100102 10010 10002 7.0 9.0 10.0
## 104 100103 10010 10003 4.0 10.0 8.0
## 105 100104 10010 10004 5.0 8.0 8.0
## 106 100105 10010 10005 6.0 9.0 7.0
## 107 100106 10010 10006 4.0 8.0 8.0
## 108 100107 10010 10007 5.0 8.0 7.0
## 109 100108 10010 10008 8.0 10.0 9.0
## 110 100109 10010 10009 6.0 10.0 10.0
## 111 100110 10011 10000 7.0 8.0 7.0
## 112 100111 10011 10001 8.0 5.0 6.0
## 113 100112 10011 10002 9.0 7.0 9.0
## 114 100113 10011 10003 8.0 7.0 8.0
## 115 100114 10011 10004 8.0 6.0 6.0
## 116 100115 10011 10005 6.0 9.0 9.0
## 117 100116 10011 10006 6.0 6.0 8.0
## 118 100117 10011 10007 8.0 7.0 7.0
## 119 100118 10011 10008 10.0 10.0 10.0
## 120 100119 10011 10009 1.0 1.0 1.0
## 121 100120 10012 10000 5.0 8.0 9.0
## 122 100121 10012 10001 5.0 8.0 9.0
## 123 100122 10012 10002 7.0 9.0 9.0
## 124 100123 10012 10003 4.0 7.0 8.0
## 125 100124 10012 10004 4.0 6.0 8.0
## 126 100125 10012 10005 4.0 7.0 9.0
## 127 100126 10012 10006 3.0 6.0 8.0
## 128 100127 10012 10007 5.0 8.0 8.0
## 129 100128 10012 10008 5.0 10.0 10.0
## 130 100129 10012 10009 6.0 10.0 10.0
## 131 100130 10013 10000 7.0 6.0 8.0
## 132 100131 10013 10001 7.0 9.0 7.0
## 133 100132 10013 10002 9.0 7.0 9.0
## 134 100133 10013 10003 8.0 10.0 7.0
## 135 100134 10013 10004 8.0 5.0 5.0
## 136 100135 10013 10005 8.0 6.0 7.0
## 137 100136 10013 10006 7.0 8.0 8.0
## 138 100137 10013 10007 8.0 6.0 5.0
## 139 100138 10013 10008 10.0 9.0 8.0
## 140 100139 10013 10009 10.0 10.0 10.0
## 141 100140 10014 10000 5.0 6.0 7.0
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## 1795 101922 10141 10166 5.0 5.0 5.0
## 1796 101923 10141 10167 7.0 9.0 9.0
## 1797 101924 10141 10168 5.0 7.0 7.0
## 1798 101925 10141 10169 5.0 8.0 9.0
## 1799 101926 10141 10170 6.0 8.0 9.0
## 1800 101927 10141 10171 4.0 6.0 5.0
## 1801 101928 10142 10156 9.0 9.0 9.0
## 1802 101929 10142 10157 8.0 8.0 8.0
## 1803 101930 10142 10158 6.0 8.0 9.0
## 1804 101931 10142 10159 6.0 8.0 10.0
## 1805 101932 10142 10160 8.0 7.0 7.0
## 1806 101933 10142 10161 8.0 8.0 8.0
## 1807 101934 10142 10162 7.0 7.0 9.0
## 1808 101935 10142 10163 7.0 8.0 10.0
## 1809 101936 10142 10164 7.0 7.0 9.0
## 1810 101937 10142 10165 4.0 6.0 5.0
## 1811 101938 10142 10166 8.0 8.0 8.0
## 1812 101939 10142 10167 8.0 7.0 8.0
## 1813 101940 10142 10168 6.0 7.0 7.0
## 1814 101941 10142 10169 8.0 9.0 9.0
## 1815 101942 10142 10170 4.0 7.0 8.0
## 1816 101943 10142 10171 5.0 7.0 6.0
## 1817 101944 10143 10156 9.0 8.0 9.0
## 1818 101945 10143 10157 8.0 8.0 8.0
## 1819 101946 10143 10158 4.0 8.0 9.0
## 1820 101947 10143 10159 6.0 8.0 10.0
## 1821 101948 10143 10160 5.0 6.0 7.0
## 1822 101949 10143 10161 6.0 7.0 6.0
## 1823 101950 10143 10162 4.0 8.0 9.0
## 1824 101951 10143 10163 6.0 8.0 9.0
## 1825 101952 10143 10164 5.0 7.0 7.0
## 1826 101953 10143 10165 2.0 6.0 6.0
## 1827 101954 10143 10166 7.0 7.0 7.0
## 1828 101955 10143 10167 7.0 9.0 9.0
## 1829 101956 10143 10168 6.0 7.0 6.0
## 1830 101957 10143 10169 6.0 8.0 9.0
## 1831 101958 10143 10170 2.0 7.0 9.0
## 1832 101959 10143 10171 4.0 6.0 6.0
## 1833 101960 10144 10156 8.0 9.0 8.0
## 1834 101961 10144 10157 6.0 8.0 8.0
## 1835 101962 10144 10158 5.0 8.0 7.0
## 1836 101963 10144 10159 5.0 6.0 7.0
## 1837 101964 10144 10160 4.0 6.0 8.0
## 1838 101965 10144 10161 5.0 8.0 7.0
## 1839 101966 10144 10162 9.0 10.0 10.0
## 1840 101967 10144 10163 8.0 7.0 9.0
## 1841 101968 10144 10164 7.0 7.0 7.0
## 1842 101969 10144 10165 3.0 6.0 8.0
## 1843 101970 10144 10166 4.0 6.0 7.0
## 1844 101971 10144 10167 7.0 8.0 9.0
## 1845 101972 10144 10168 5.0 7.0 7.0
## 1846 101973 10144 10169 6.0 8.0 9.0
## 1847 101974 10144 10170 1.0 4.0 5.0
## 1848 101975 10144 10171 5.0 6.0 5.0
## 1849 101976 10145 10156 9.0 7.0 7.0
## 1850 101977 10145 10157 6.0 6.0 6.0
## 1851 101978 10145 10158 8.0 10.0 9.0
## 1852 101979 10145 10159 7.0 8.0 9.0
## 1853 101980 10145 10160 5.0 7.0 7.0
## 1854 101981 10145 10161 7.0 9.0 7.0
## 1855 101982 10145 10162 9.0 6.0 6.0
## 1856 101983 10145 10163 7.0 8.0 8.0
## 1857 101984 10145 10164 8.0 8.0 8.0
## 1858 101985 10145 10165 3.0 6.0 5.0
## 1859 101986 10145 10166 4.0 7.0 8.0
## 1860 101987 10145 10167 10.0 8.0 8.0
## 1861 101988 10145 10168 7.0 7.0 7.0
## 1862 101989 10145 10169 5.0 6.0 7.0
## 1863 101990 10145 10170 8.0 4.0 7.0
## 1864 101991 10145 10171 5.0 7.0 5.0
## 1865 101992 10146 10156 9.0 8.0 8.0
## 1866 101993 10146 10157 7.0 6.0 6.0
## 1867 101994 10146 10158 7.0 8.0 8.0
## 1868 101995 10146 10159 6.0 6.0 8.0
## 1869 101996 10146 10160 6.0 6.0 7.0
## 1870 101997 10146 10161 8.0 8.0 8.0
## 1871 101998 10146 10162 7.0 7.0 7.0
## 1872 101999 10146 10163 8.0 8.0 9.0
## 1873 102000 10146 10164 8.0 8.0 8.0
## 1874 102001 10146 10165 6.0 5.0 6.0
## 1875 102002 10146 10166 7.0 8.0 8.0
## 1876 102003 10146 10167 9.0 9.0 9.0
## 1877 102004 10146 10168 6.0 6.0 7.0
## 1878 102005 10146 10169 7.0 7.0 7.0
## 1879 102006 10146 10170 7.0 4.0 6.0
## 1880 102007 10146 10171 5.0 8.0 5.0
## 1881 102008 10147 10156 9.0 9.0 9.0
## 1882 102009 10147 10157 8.0 9.0 7.0
## 1883 102010 10147 10158 6.0 9.0 9.0
## 1884 102011 10147 10159 7.0 6.0 7.0
## 1885 102012 10147 10160 8.0 6.0 6.0
## 1886 102013 10147 10161 8.0 8.0 8.0
## 1887 102014 10147 10162 6.0 6.0 6.0
## 1888 102015 10147 10163 9.0 7.0 9.0
## 1889 102016 10147 10164 7.0 7.0 7.0
## 1890 102017 10147 10165 6.0 6.0 6.0
## 1891 102018 10147 10166 5.0 6.0 6.0
## 1892 102019 10147 10167 9.0 8.0 9.0
## 1893 102020 10147 10168 7.0 7.0 7.0
## 1894 102021 10147 10169 8.0 7.0 8.0
## 1895 102022 10147 10170 6.0 4.0 7.0
## 1896 102023 10147 10171 8.0 8.0 8.0
## 1897 102024 10148 10156 10.0 8.0 9.0
## 1898 102025 10148 10157 8.0 9.0 9.0
## 1899 102026 10148 10158 7.0 10.0 9.0
## 1900 102027 10148 10159 8.0 8.0 10.0
## 1901 102028 10148 10160 6.0 9.0 8.0
## 1902 102029 10148 10161 9.0 9.0 9.0
## 1903 102030 10148 10162 9.0 10.0 9.0
## 1904 102031 10148 10163 8.0 10.0 9.0
## 1905 102032 10148 10164 5.0 7.0 9.0
## 1906 102033 10148 10165 7.0 5.0 6.0
## 1907 102034 10148 10166 8.0 7.0 7.0
## 1908 102035 10148 10167 7.0 8.0 9.0
## 1909 102036 10148 10168 6.0 7.0 7.0
## 1910 102037 10148 10169 7.0 9.0 9.0
## 1911 102038 10148 10170 4.0 6.0 8.0
## 1912 102039 10148 10171 7.0 7.0 7.0
## 1913 102040 10149 10156 10.0 8.0 8.0
## 1914 102041 10149 10157 8.0 8.0 7.0
## 1915 102042 10149 10158 8.0 8.0 7.0
## 1916 102043 10149 10159 10.0 8.0 8.0
## 1917 102044 10149 10160 8.0 7.0 7.0
## 1918 102045 10149 10161 8.0 9.0 8.0
## 1919 102046 10149 10162 9.0 9.0 7.0
## 1920 102047 10149 10163 10.0 9.0 8.0
## 1921 102048 10149 10164 8.0 8.0 8.0
## 1922 102049 10149 10165 8.0 8.0 5.0
## 1923 102050 10149 10166 9.0 8.0 7.0
## 1924 102051 10149 10167 9.0 9.0 9.0
## 1925 102052 10149 10168 8.0 7.0 7.0
## 1926 102053 10149 10169 9.0 9.0 8.0
## 1927 102054 10149 10170 8.0 7.0 8.0
## 1928 102055 10149 10171 9.0 8.0 7.0
## 1929 102056 10150 10156 9.0 9.0 8.0
## 1930 102057 10150 10157 7.0 7.0 7.0
## 1931 102058 10150 10158 7.0 9.0 10.0
## 1932 102059 10150 10159 7.0 10.0 9.0
## 1933 102060 10150 10160 6.0 7.0 7.0
## 1934 102061 10150 10161 6.0 8.0 8.0
## 1935 102062 10150 10162 8.0 9.0 9.0
## 1936 102063 10150 10163 8.0 9.0 10.0
## 1937 102064 10150 10164 8.0 8.0 7.0
## 1938 102065 10150 10165 7.0 8.0 8.0
## 1939 102066 10150 10166 7.0 7.0 7.0
## 1940 102067 10150 10167 10.0 9.0 8.0
## 1941 102068 10150 10168 5.0 7.0 6.0
## 1942 102069 10150 10169 7.0 7.0 8.0
## 1943 102070 10150 10170 7.0 8.0 8.0
## 1944 102071 10150 10171 5.0 5.0 5.0
## 1945 102072 10151 10156 7.0 9.0 8.0
## 1946 102073 10151 10157 7.0 7.0 7.0
## 1947 102074 10151 10158 7.0 9.0 8.0
## 1948 102075 10151 10159 7.0 9.0 6.0
## 1949 102076 10151 10160 5.0 7.0 6.0
## 1950 102077 10151 10161 8.0 10.0 8.0
## 1951 102078 10151 10162 7.0 8.0 8.0
## 1952 102079 10151 10163 7.0 8.0 8.0
## 1953 102080 10151 10164 6.0 7.0 7.0
## 1954 102081 10151 10165 4.0 8.0 6.0
## 1955 102082 10151 10166 7.0 7.0 7.0
## 1956 102083 10151 10167 9.0 10.0 8.0
## 1957 102084 10151 10168 5.0 7.0 7.0
## 1958 102085 10151 10169 6.0 8.0 8.0
## 1959 102086 10151 10170 4.0 6.0 8.0
## 1960 102087 10151 10171 5.0 5.0 5.0
## 1961 102088 10152 10156 9.0 9.0 7.0
## 1962 102089 10152 10157 7.0 8.0 7.0
## 1963 102090 10152 10158 8.0 8.0 8.0
## 1964 102091 10152 10159 9.0 9.0 8.0
## 1965 102092 10152 10160 7.0 6.0 7.0
## 1966 102093 10152 10161 8.0 10.0 8.0
## 1967 102094 10152 10162 8.0 7.0 7.0
## 1968 102095 10152 10163 10.0 8.0 8.0
## 1969 102096 10152 10164 7.0 7.0 7.0
## 1970 102097 10152 10165 5.0 7.0 7.0
## 1971 102098 10152 10166 8.0 8.0 8.0
## 1972 102099 10152 10167 10.0 9.0 9.0
## 1973 102100 10152 10168 6.0 6.0 7.0
## 1974 102101 10152 10169 9.0 7.0 8.0
## 1975 102102 10152 10170 7.0 8.0 8.0
## 1976 102103 10152 10171 6.0 7.0 6.0
## 1977 102104 10153 10156 10.0 9.0 9.0
## 1978 102105 10153 10157 7.0 9.0 8.0
## 1979 102106 10153 10158 7.0 8.0 8.0
## 1980 102107 10153 10159 7.0 8.0 8.0
## 1981 102108 10153 10160 6.0 5.0 3.0
## 1982 102109 10153 10161 8.0 6.0 6.0
## 1983 102110 10153 10162 5.0 10.0 8.0
## 1984 102111 10153 10163 9.0 9.0 8.0
## 1985 102112 10153 10164 7.0 7.0 7.0
## 1986 102113 10153 10165 6.0 6.0 5.0
## 1987 102114 10153 10166 7.0 7.0 7.0
## 1988 102115 10153 10167 9.0 8.0 8.0
## 1989 102116 10153 10168 6.0 7.0 7.0
## 1990 102117 10153 10169 10.0 8.0 8.0
## 1991 102118 10153 10170 5.0 6.0 6.0
## 1992 102119 10153 10171 6.0 7.0 7.0
## 1993 102120 10154 10156 9.0 8.0 8.0
## 1994 102121 10154 10157 6.0 7.0 7.0
## 1995 102122 10154 10158 8.0 10.0 8.0
## 1996 102123 10154 10159 7.0 9.0 10.0
## 1997 102124 10154 10160 5.0 7.0 7.0
## 1998 102125 10154 10161 8.0 9.0 9.0
## 1999 102126 10154 10162 8.0 8.0 10.0
## 2000 102127 10154 10163 7.0 8.0 7.0
## 2001 102128 10154 10164 7.0 7.0 7.0
## 2002 102129 10154 10165 7.0 6.0 6.0
## 2003 102130 10154 10166 7.0 8.0 8.0
## 2004 102131 10154 10167 8.0 8.0 10.0
## 2005 102132 10154 10168 6.0 8.0 8.0
## 2006 102133 10154 10169 6.0 8.0 9.0
## 2007 102134 10154 10170 6.0 8.0 7.0
## 2008 102135 10154 10171 7.0 7.0 7.0
## 2009 102136 10155 10156 9.0 8.0 8.0
## 2010 102137 10155 10157 6.0 10.0 6.0
## 2011 102138 10155 10158 10.0 8.0 8.0
## 2012 102139 10155 10159 8.0 8.0 8.0
## 2013 102140 10155 10160 5.0 5.0 6.0
## 2014 102141 10155 10161 7.0 6.0 6.0
## 2015 102142 10155 10162 8.0 7.0 8.0
## 2016 102143 10155 10163 9.0 7.0 8.0
## 2017 102144 10155 10164 8.0 8.0 8.0
## 2018 102145 10155 10165 6.0 7.0 6.0
## 2019 102146 10155 10166 6.0 8.0 8.0
## 2020 102147 10155 10167 10.0 8.0 8.0
## 2021 102148 10155 10168 6.0 6.0 6.0
## 2022 102149 10155 10169 9.0 8.0 9.0
## 2023 102150 10155 10170 8.0 5.0 7.0
## 2024 102151 10155 10171 7.0 8.0 8.0
## 2025 102152 10156 10140 4.0 5.0 6.0
## 2026 102153 10156 10141 5.0 6.0 8.0
## 2027 102154 10156 10142 4.0 6.0 7.0
## 2028 102155 10156 10143 7.0 10.0 10.0
## 2029 102156 10156 10144 4.0 6.0 8.0
## 2030 102157 10156 10145 5.0 5.0 8.0
## 2031 102158 10156 10146 4.0 8.0 8.0
## 2032 102159 10156 10147 6.0 7.0 7.0
## 2033 102160 10156 10148 5.0 5.0 8.0
## 2034 102161 10156 10149 4.0 5.0 5.0
## 2035 102162 10156 10150 6.0 7.0 7.0
## 2036 102163 10156 10151 6.0 6.0 8.0
## 2037 102164 10156 10152 5.0 8.0 10.0
## 2038 102165 10156 10153 2.0 6.0 7.0
## 2039 102166 10156 10154 4.0 7.0 10.0
## 2040 102167 10156 10155 2.0 3.0 3.0
## 2041 102168 10157 10140 5.0 10.0 9.0
## 2042 102169 10157 10141 4.0 6.0 8.0
## 2043 102170 10157 10142 4.0 7.0 8.0
## 2044 102171 10157 10143 7.0 10.0 10.0
## 2045 102172 10157 10144 5.0 7.0 10.0
## 2046 102173 10157 10145 5.0 6.0 8.0
## 2047 102174 10157 10146 3.0 8.0 8.0
## 2048 102175 10157 10147 4.0 7.0 8.0
## 2049 102176 10157 10148 5.0 8.0 10.0
## 2050 102177 10157 10149 3.0 7.0 7.0
## 2051 102178 10157 10150 4.0 6.0 5.0
## 2052 102179 10157 10151 5.0 5.0 6.0
## 2053 102180 10157 10152 6.0 8.0 9.0
## 2054 102181 10157 10153 1.0 4.0 6.0
## 2055 102182 10157 10154 3.0 10.0 10.0
## 2056 102183 10157 10155 2.0 6.0 6.0
## 2057 102184 10158 10140 8.0 8.0 8.0
## 2058 102185 10158 10141 5.0 5.0 7.0
## 2059 102186 10158 10142 7.0 8.0 8.0
## 2060 102187 10158 10143 8.0 10.0 7.0
## 2061 102188 10158 10144 8.0 10.0 7.0
## 2062 102189 10158 10145 8.0 6.0 7.0
## 2063 102190 10158 10146 7.0 5.0 7.0
## 2064 102191 10158 10147 9.0 7.0 8.0
## 2065 102192 10158 10148 9.0 7.0 7.0
## 2066 102193 10158 10149 8.0 6.0 8.0
## 2067 102194 10158 10150 6.0 8.0 7.0
## 2068 102195 10158 10151 5.0 6.0 6.0
## 2069 102196 10158 10152 9.0 9.0 9.0
## 2070 102197 10158 10153 7.0 7.0 6.0
## 2071 102198 10158 10154 7.0 10.0 10.0
## 2072 102199 10158 10155 7.0 6.0 7.0
## 2073 102200 10159 10140 10.0 10.0 9.0
## 2074 102201 10159 10141 5.0 6.0 8.0
## 2075 102202 10159 10142 8.0 8.0 8.0
## 2076 102203 10159 10143 10.0 9.0 9.0
## 2077 102204 10159 10144 6.0 8.0 8.0
## 2078 102205 10159 10145 7.0 6.0 7.0
## 2079 102206 10159 10146 5.0 8.0 8.0
## 2080 102207 10159 10147 8.0 6.0 8.0
## 2081 102208 10159 10148 8.0 5.0 8.0
## 2082 102209 10159 10149 9.0 7.0 8.0
## 2083 102210 10159 10150 8.0 8.0 8.0
## 2084 102211 10159 10151 7.0 7.0 7.0
## 2085 102212 10159 10152 10.0 8.0 9.0
## 2086 102213 10159 10153 8.0 7.0 7.0
## 2087 102214 10159 10154 8.0 9.0 10.0
## 2088 102215 10159 10155 7.0 7.0 6.0
## 2089 102216 10160 10140 8.0 10.0 9.0
## 2090 102217 10160 10141 6.0 7.0 9.0
## 2091 102218 10160 10142 7.0 7.0 8.0
## 2092 102219 10160 10143 9.0 9.0 9.0
## 2093 102220 10160 10144 5.0 5.0 10.0
## 2094 102221 10160 10145 5.0 6.0 6.0
## 2095 102222 10160 10146 6.0 8.0 8.0
## 2096 102223 10160 10147 4.0 8.0 7.0
## 2097 102224 10160 10148 6.0 6.0 7.0
## 2098 102225 10160 10149 9.0 8.0 7.0
## 2099 102226 10160 10150 5.0 6.0 8.0
## 2100 102227 10160 10151 5.0 5.0 7.0
## 2101 102228 10160 10152 5.0 8.0 8.0
## 2102 102229 10160 10153 2.0 4.0 6.0
## 2103 102230 10160 10154 7.0 6.0 10.0
## 2104 102231 10160 10155 3.0 5.0 7.0
## 2105 102232 10161 10140 6.0 8.0 8.0
## 2106 102233 10161 10141 9.0 8.0 9.0
## 2107 102234 10161 10142 6.0 7.0 7.0
## 2108 102235 10161 10143 9.0 9.0 8.0
## 2109 102236 10161 10144 6.0 8.0 8.0
## 2110 102237 10161 10145 7.0 8.0 8.0
## 2111 102238 10161 10146 7.0 9.0 9.0
## 2112 102239 10161 10147 5.0 6.0 6.0
## 2113 102240 10161 10148 7.0 7.0 8.0
## 2114 102241 10161 10149 7.0 5.0 7.0
## 2115 102242 10161 10150 7.0 8.0 7.0
## 2116 102243 10161 10151 8.0 8.0 8.0
## 2117 102244 10161 10152 9.0 9.0 8.0
## 2118 102245 10161 10153 6.0 7.0 6.0
## 2119 102246 10161 10154 6.0 9.0 10.0
## 2120 102247 10161 10155 5.0 7.0 6.0
## 2121 102248 10162 10140 8.0 9.0 10.0
## 2122 102249 10162 10141 8.0 7.0 9.0
## 2123 102250 10162 10142 8.0 8.0 7.0
## 2124 102251 10162 10143 9.0 10.0 10.0
## 2125 102252 10162 10144 10.0 9.0 10.0
## 2126 102253 10162 10145 6.0 8.0 8.0
## 2127 102254 10162 10146 4.0 6.0 7.0
## 2128 102255 10162 10147 3.0 7.0 8.0
## 2129 102256 10162 10148 8.0 7.0 6.0
## 2130 102257 10162 10149 6.0 8.0 8.0
## 2131 102258 10162 10150 6.0 8.0 8.0
## 2132 102259 10162 10151 6.0 6.0 7.0
## 2133 102260 10162 10152 7.0 9.0 9.0
## 2134 102261 10162 10153 6.0 8.0 7.0
## 2135 102262 10162 10154 7.0 9.0 10.0
## 2136 102263 10162 10155 5.0 6.0 6.0
## 2137 102264 10163 10140 6.0 8.0 7.0
## 2138 102265 10163 10141 10.0 9.0 9.0
## 2139 102266 10163 10142 7.0 6.0 6.0
## 2140 102267 10163 10143 8.0 8.0 7.0
## 2141 102268 10163 10144 8.0 8.0 7.0
## 2142 102269 10163 10145 7.0 7.0 7.0
## 2143 102270 10163 10146 6.0 7.0 6.0
## 2144 102271 10163 10147 8.0 7.0 6.0
## 2145 102272 10163 10148 9.0 8.0 8.0
## 2146 102273 10163 10149 8.0 5.0 5.0
## 2147 102274 10163 10150 8.0 8.0 7.0
## 2148 102275 10163 10151 5.0 5.0 5.0
## 2149 102276 10163 10152 7.0 9.0 7.0
## 2150 102277 10163 10153 7.0 6.0 5.0
## 2151 102278 10163 10154 8.0 5.0 10.0
## 2152 102279 10163 10155 6.0 7.0 7.0
## 2153 102280 10164 10140 8.0 6.0 6.0
## 2154 102281 10164 10141 9.0 8.0 9.0
## 2155 102282 10164 10142 7.0 8.0 8.0
## 2156 102283 10164 10143 7.0 7.0 7.0
## 2157 102284 10164 10144 5.0 8.0 6.0
## 2158 102285 10164 10145 8.0 8.0 8.0
## 2159 102286 10164 10146 7.0 8.0 7.0
## 2160 102287 10164 10147 8.0 5.0 6.0
## 2161 102288 10164 10148 6.0 5.0 6.0
## 2162 102289 10164 10149 6.0 4.0 6.0
## 2163 102290 10164 10150 5.0 5.0 6.0
## 2164 102291 10164 10151 4.0 4.0 5.0
## 2165 102292 10164 10152 9.0 9.0 9.0
## 2166 102293 10164 10153 8.0 7.0 7.0
## 2167 102294 10164 10154 7.0 6.0 10.0
## 2168 102295 10164 10155 6.0 7.0 7.0
## 2169 102296 10165 10140 8.0 8.0 8.0
## 2170 102297 10165 10141 5.0 6.0 7.0
## 2171 102298 10165 10142 6.0 7.0 8.0
## 2172 102299 10165 10143 10.0 9.0 9.0
## 2173 102300 10165 10144 5.0 7.0 8.0
## 2174 102301 10165 10145 6.0 6.0 6.0
## 2175 102302 10165 10146 3.0 8.0 8.0
## 2176 102303 10165 10147 4.0 6.0 7.0
## 2177 102304 10165 10148 7.0 7.0 8.0
## 2178 102305 10165 10149 6.0 3.0 6.0
## 2179 102306 10165 10150 7.0 9.0 8.0
## 2180 102307 10165 10151 5.0 6.0 7.0
## 2181 102308 10165 10152 8.0 8.0 8.0
## 2182 102309 10165 10153 5.0 5.0 6.0
## 2183 102310 10165 10154 6.0 9.0 10.0
## 2184 102311 10165 10155 4.0 5.0 5.0
## 2185 102312 10166 10140 6.0 9.0 9.0
## 2186 102313 10166 10141 7.0 8.0 9.0
## 2187 102314 10166 10142 5.0 5.0 6.0
## 2188 102315 10166 10143 6.0 9.0 8.0
## 2189 102316 10166 10144 6.0 8.0 10.0
## 2190 102317 10166 10145 4.0 8.0 8.0
## 2191 102318 10166 10146 2.0 8.0 8.0
## 2192 102319 10166 10147 4.0 7.0 8.0
## 2193 102320 10166 10148 7.0 8.0 8.0
## 2194 102321 10166 10149 6.0 3.0 7.0
## 2195 102322 10166 10150 4.0 7.0 7.0
## 2196 102323 10166 10151 5.0 6.0 6.0
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## 1749 9.0 6.0 9.0 8.0 8.0
## 1750 8.0 6.0 8.0 6.0 10.0
## 1751 9.0 10.0 8.0 9.0 9.0
## 1752 7.0 7.0 5.0 7.0 8.0
## 1753 8.0 8.0 8.0 8.0 7.0
## 1754 7.0 8.0 6.0 3.0 8.0
## 1755 8.0 7.0 8.0 3.0 8.0
## 1756 7.0 7.0 2.0 4.0 6.0
## 1757 5.0 9.0 5.0 8.0 7.0
## 1758 6.0 6.0 7.0 6.0 8.0
## 1759 5.0 8.0 5.0 7.0 9.0
## 1760 8.0 4.0 6.0 6.0 7.0
## 1761 9.0 9.0 8.0 7.0 7.0
## 1762 8.0 8.0 7.0 5.0 9.0
## 1763 9.0 10.0 7.0 6.5 8.0
## 1764 8.0 7.0 6.0 6.5 8.0
## 1765 8.0 7.0 7.0 8.0 7.0
## 1766 7.0 5.0 6.0 6.0 10.0
## 1767 5.0 6.0 4.0 7.0 8.0
## 1768 8.0 8.0 6.0 8.0 9.0
## 1769 7.0 7.0 5.0 4.0 5.0
## 1770 6.0 7.0 4.0 5.0 10.0
## 1771 7.0 9.0 6.0 8.0 8.0
## 1772 7.0 6.0 4.0 10.0 10.0
## 1773 6.0 6.0 5.0 8.0 10.0
## 1774 5.0 6.0 5.0 6.0 8.0
## 1775 7.0 7.0 6.0 8.0 9.0
## 1776 7.0 8.0 6.0 6.0 8.0
## 1777 4.0 7.0 6.0 8.0 6.0
## 1778 6.0 4.0 4.0 8.0 8.0
## 1779 7.0 7.0 8.0 6.0 9.0
## 1780 8.0 7.0 8.0 10.0 10.0
## 1781 7.0 5.0 7.0 4.0 7.0
## 1782 9.0 8.0 7.0 4.0 10.0
## 1783 7.0 7.0 6.0 8.0 7.0
## 1784 7.0 5.0 5.0 6.0 4.0
## 1785 6.0 8.0 7.0 5.0 6.0
## 1786 5.0 4.0 4.0 4.0 6.0
## 1787 5.0 8.0 3.0 5.0 5.0
## 1788 5.0 10.0 6.0 5.0 6.0
## 1789 2.0 7.0 2.0 6.0 7.0
## 1790 6.0 6.0 4.0 9.0 8.0
## 1791 5.0 7.0 6.0 8.0 7.0
## 1792 6.0 8.0 4.0 10.0 9.0
## 1793 5.0 7.0 6.0 9.0 8.0
## 1794 4.0 5.0 2.0 5.0 6.0
## 1795 6.0 6.0 6.0 7.0 8.0
## 1796 8.0 8.0 8.0 8.0 9.0
## 1797 5.0 7.0 3.0 5.0 5.0
## 1798 6.0 7.0 9.0 8.0 9.0
## 1799 2.0 8.0 1.0 8.0 8.0
## 1800 4.0 5.0 5.0 7.0 5.0
## 1801 7.0 7.0 8.0 4.0 6.0
## 1802 8.0 7.0 9.0 4.0 7.0
## 1803 9.0 9.0 7.0 7.0 8.0
## 1804 9.0 3.0 1.0 8.0 8.0
## 1805 7.0 7.0 5.0 7.0 7.0
## 1806 6.0 6.0 5.0 6.0 7.0
## 1807 7.0 6.0 5.0 8.0 8.0
## 1808 8.0 7.0 6.0 7.0 6.0
## 1809 9.0 7.0 6.0 7.0 8.0
## 1810 4.0 5.0 6.0 6.0 7.0
## 1811 8.0 8.0 8.0 5.0 5.0
## 1812 9.0 7.0 9.0 7.0 7.0
## 1813 7.0 7.0 6.0 5.0 7.0
## 1814 9.0 8.0 10.0 5.0 7.0
## 1815 5.0 6.0 4.0 5.0 8.0
## 1816 6.0 6.0 5.0 6.0 7.0
## 1817 7.0 7.0 8.0 7.0 10.0
## 1818 7.0 7.0 9.0 7.0 10.0
## 1819 4.0 10.0 3.0 8.0 10.0
## 1820 7.0 5.0 3.0 10.0 9.0
## 1821 7.0 6.0 5.0 9.0 9.0
## 1822 8.0 5.0 3.0 9.0 9.0
## 1823 8.0 7.0 7.0 9.0 10.0
## 1824 6.0 8.0 5.0 8.0 8.0
## 1825 6.0 7.0 6.0 7.0 7.0
## 1826 4.0 5.0 5.0 10.0 9.0
## 1827 7.0 7.0 7.0 6.0 9.0
## 1828 8.0 9.0 8.0 9.0 7.0
## 1829 6.0 6.0 8.0 8.0 10.0
## 1830 5.0 8.0 3.0 9.0 9.0
## 1831 2.0 4.0 2.0 9.0 9.0
## 1832 5.0 5.0 5.0 8.0 10.0
## 1833 7.0 7.0 8.0 4.0 6.0
## 1834 5.0 6.0 6.0 5.0 7.0
## 1835 6.0 7.0 6.0 8.0 10.0
## 1836 4.0 5.0 5.0 6.0 8.0
## 1837 3.0 6.0 3.0 5.0 5.0
## 1838 6.0 7.0 7.0 6.0 8.0
## 1839 8.0 8.0 8.0 10.0 9.0
## 1840 7.0 7.0 7.0 8.0 8.0
## 1841 7.0 7.0 6.0 5.0 8.0
## 1842 2.0 5.0 3.0 5.0 7.0
## 1843 6.0 6.0 3.0 6.0 8.0
## 1844 7.0 8.0 7.0 8.0 8.0
## 1845 6.0 6.0 5.0 6.0 8.0
## 1846 6.0 7.0 7.0 8.0 8.0
## 1847 1.0 3.0 1.0 8.0 8.0
## 1848 5.0 5.0 4.0 6.0 8.0
## 1849 9.0 8.0 6.0 5.0 5.0
## 1850 6.0 6.0 3.0 5.0 6.0
## 1851 8.0 8.0 7.0 8.0 6.0
## 1852 10.0 6.0 6.0 7.0 6.0
## 1853 8.0 7.0 5.0 5.0 6.0
## 1854 8.0 7.0 8.0 7.0 8.0
## 1855 8.0 6.0 5.0 6.0 8.0
## 1856 10.0 8.0 5.0 7.0 7.0
## 1857 9.0 7.0 6.0 8.0 8.0
## 1858 8.0 2.0 4.0 6.0 6.0
## 1859 7.0 5.0 7.0 4.0 8.0
## 1860 9.0 8.0 6.0 8.0 7.0
## 1861 6.0 6.0 7.0 4.0 7.0
## 1862 9.0 7.0 4.0 4.0 7.0
## 1863 7.0 8.0 6.0 8.0 10.0
## 1864 6.0 6.0 4.0 6.0 8.0
## 1865 7.0 7.0 7.0 4.0 8.0
## 1866 5.0 5.0 6.0 3.0 8.0
## 1867 8.0 8.0 7.0 7.0 5.0
## 1868 5.0 7.0 5.0 5.0 8.0
## 1869 7.0 6.0 7.0 6.0 8.0
## 1870 7.0 6.0 5.0 7.0 9.0
## 1871 7.0 7.0 6.0 4.0 6.0
## 1872 6.0 9.0 5.0 6.0 7.0
## 1873 8.0 8.0 6.0 7.0 8.0
## 1874 5.0 4.0 3.0 3.0 8.0
## 1875 6.0 6.0 5.0 2.0 8.0
## 1876 7.0 9.0 6.0 8.0 8.0
## 1877 5.0 8.0 5.0 3.0 8.0
## 1878 6.0 8.0 6.0 4.0 8.0
## 1879 9.0 8.0 4.0 7.0 8.0
## 1880 7.0 6.0 5.0 7.0 9.0
## 1881 8.0 7.0 6.0 6.0 7.0
## 1882 7.0 7.0 6.0 4.0 7.0
## 1883 6.0 8.0 6.0 9.0 7.0
## 1884 5.0 8.0 7.0 8.0 6.0
## 1885 7.0 7.0 5.0 4.0 8.0
## 1886 7.0 7.0 6.0 5.0 6.0
## 1887 6.0 7.0 6.0 3.0 7.0
## 1888 7.0 6.0 5.0 8.0 7.0
## 1889 7.0 7.0 6.0 8.0 5.0
## 1890 5.0 4.0 4.0 4.0 6.0
## 1891 6.0 6.0 6.0 4.0 7.0
## 1892 8.0 8.0 8.0 8.0 7.0
## 1893 6.0 6.0 6.0 4.0 8.0
## 1894 6.0 9.0 10.0 6.0 8.0
## 1895 5.0 6.0 7.0 7.0 7.0
## 1896 8.0 7.0 8.0 5.0 8.0
## 1897 7.0 7.0 7.0 5.0 5.0
## 1898 8.0 8.0 5.0 5.0 8.0
## 1899 8.0 8.0 3.0 9.0 7.0
## 1900 8.0 6.0 5.0 8.0 5.0
## 1901 5.0 6.0 3.0 6.0 6.0
## 1902 8.0 8.0 5.0 7.0 7.0
## 1903 9.0 7.0 9.0 8.0 7.0
## 1904 8.0 8.0 7.0 9.0 8.0
## 1905 8.0 9.0 6.0 6.0 5.0
## 1906 7.0 5.0 4.0 7.0 7.0
## 1907 7.0 7.0 7.0 7.0 8.0
## 1908 7.0 9.0 8.0 9.0 9.0
## 1909 7.0 6.0 7.0 5.0 6.0
## 1910 7.0 8.0 5.0 6.0 7.0
## 1911 4.0 6.0 3.0 6.0 7.0
## 1912 7.0 7.0 5.0 6.0 8.0
## 1913 9.0 7.0 6.0 4.0 5.0
## 1914 7.0 7.0 6.0 3.0 7.0
## 1915 8.0 7.0 8.0 8.0 6.0
## 1916 9.0 8.0 5.0 9.0 7.0
## 1917 7.0 6.0 9.0 9.0 8.0
## 1918 7.0 6.0 5.0 7.0 5.0
## 1919 9.0 7.0 6.0 6.0 8.0
## 1920 10.0 7.0 3.0 8.0 5.0
## 1921 8.0 7.0 6.0 6.0 4.0
## 1922 4.0 3.0 4.0 6.0 3.0
## 1923 7.0 8.0 7.0 6.0 3.0
## 1924 9.0 8.0 7.0 7.0 8.0
## 1925 8.0 7.0 5.0 3.0 8.0
## 1926 8.0 7.0 6.0 5.0 7.0
## 1927 6.0 8.0 6.0 7.0 8.0
## 1928 8.0 7.0 7.0 8.0 7.0
## 1929 7.0 7.0 6.0 6.0 7.0
## 1930 5.0 7.0 3.0 4.0 6.0
## 1931 6.0 9.0 3.0 6.0 8.0
## 1932 6.0 5.0 2.0 8.0 8.0
## 1933 5.0 6.0 4.0 5.0 6.0
## 1934 6.0 7.0 5.0 7.0 8.0
## 1935 7.0 7.0 6.0 6.0 8.0
## 1936 5.0 9.0 5.0 8.0 8.0
## 1937 5.0 7.0 6.0 5.0 5.0
## 1938 6.0 5.0 3.0 7.0 9.0
## 1939 7.0 7.0 6.0 4.0 7.0
## 1940 7.0 8.0 6.0 8.0 9.0
## 1941 5.0 6.0 5.0 4.0 6.0
## 1942 5.0 7.0 4.0 6.0 7.0
## 1943 4.0 6.0 3.0 6.0 8.0
## 1944 5.0 5.0 5.0 5.0 8.0
## 1945 6.0 7.0 6.0 6.0 6.0
## 1946 6.0 6.0 5.0 5.0 5.0
## 1947 6.0 7.0 2.0 5.0 6.0
## 1948 6.0 5.0 3.0 7.0 7.0
## 1949 3.0 7.0 3.0 5.0 5.0
## 1950 8.0 8.0 6.0 8.0 8.0
## 1951 6.0 7.0 6.0 6.0 6.0
## 1952 6.0 7.0 5.0 5.0 5.0
## 1953 4.0 7.0 6.0 4.0 4.0
## 1954 5.0 6.0 4.0 5.0 6.0
## 1955 6.0 6.0 5.0 5.0 6.0
## 1956 7.0 8.0 6.0 7.0 7.0
## 1957 6.0 5.0 7.0 5.0 5.0
## 1958 6.0 7.0 4.0 6.0 6.0
## 1959 2.0 7.0 1.0 6.0 5.0
## 1960 5.0 5.0 5.0 5.0 5.0
## 1961 8.0 7.0 6.0 5.0 8.0
## 1962 7.0 8.0 6.0 6.0 8.0
## 1963 7.0 8.0 7.0 9.0 9.0
## 1964 9.0 7.0 6.0 10.0 8.0
## 1965 6.0 7.0 3.0 5.0 8.0
## 1966 8.0 7.0 5.0 9.0 9.0
## 1967 7.0 7.0 6.0 7.0 9.0
## 1968 8.0 10.0 3.0 7.0 9.0
## 1969 7.0 7.0 6.0 9.0 9.0
## 1970 6.0 6.0 6.0 8.0 8.0
## 1971 8.0 8.0 8.0 5.0 9.0
## 1972 9.0 9.0 10.0 9.0 9.0
## 1973 5.0 7.0 7.0 6.0 10.0
## 1974 8.0 10.0 5.0 8.0 9.0
## 1975 5.0 6.0 6.0 9.0 10.0
## 1976 7.0 6.0 6.0 9.0 9.0
## 1977 8.0 7.0 7.0 2.0 6.0
## 1978 7.0 5.0 6.0 1.0 4.0
## 1979 8.0 7.0 6.0 7.0 7.0
## 1980 10.0 7.0 6.0 8.0 7.0
## 1981 4.0 7.0 3.0 2.0 4.0
## 1982 6.0 6.0 7.0 6.0 7.0
## 1983 7.0 7.0 5.0 6.0 8.0
## 1984 6.0 5.0 5.0 7.0 6.0
## 1985 7.0 7.0 6.0 8.0 7.0
## 1986 4.0 4.0 3.0 5.0 5.0
## 1987 7.0 7.0 7.0 3.0 6.0
## 1988 8.0 8.0 8.0 8.0 8.0
## 1989 6.0 6.0 6.0 2.0 8.0
## 1990 8.0 9.0 6.0 4.0 7.0
## 1991 4.0 5.0 5.0 5.0 8.0
## 1992 8.0 7.0 5.0 3.0 8.0
## 1993 8.0 7.0 6.0 4.0 7.0
## 1994 5.0 5.0 6.0 3.0 10.0
## 1995 9.0 9.0 10.0 7.0 10.0
## 1996 8.0 8.0 4.0 8.0 9.0
## 1997 5.0 9.0 6.0 7.0 6.0
## 1998 8.0 8.0 8.0 6.0 9.0
## 1999 7.0 9.0 5.0 7.0 9.0
## 2000 7.0 8.0 6.0 8.0 5.0
## 2001 7.0 7.0 6.0 7.0 6.0
## 2002 5.0 6.0 4.0 6.0 9.0
## 2003 7.0 7.0 6.0 6.0 9.0
## 2004 8.0 10.0 8.0 7.0 9.0
## 2005 7.0 8.0 8.0 4.0 8.0
## 2006 9.0 10.0 4.0 5.0 8.0
## 2007 7.0 8.0 7.0 5.0 9.0
## 2008 7.0 7.0 6.0 6.0 9.0
## 2009 7.0 6.0 5.0 2.0 3.0
## 2010 6.0 7.0 6.0 2.0 6.0
## 2011 9.0 9.0 8.0 7.0 6.0
## 2012 9.0 7.0 6.0 7.0 7.0
## 2013 6.0 7.0 3.0 3.0 5.0
## 2014 6.0 7.0 4.0 5.0 7.0
## 2015 9.0 7.0 6.0 5.0 6.0
## 2016 6.0 8.0 5.0 6.0 7.0
## 2017 8.0 7.0 6.0 6.0 7.0
## 2018 6.0 5.0 4.0 4.0 5.0
## 2019 8.0 8.0 8.0 2.0 8.0
## 2020 9.0 9.0 8.0 8.0 7.0
## 2021 5.0 7.0 6.0 3.0 6.0
## 2022 9.0 10.0 5.0 5.0 7.0
## 2023 8.0 9.0 6.0 7.0 7.0
## 2024 8.0 8.0 7.0 5.0 6.0
## 2025 4.0 6.0 4.0 10.0 8.0
## 2026 6.0 7.0 6.0 6.0 8.0
## 2027 4.0 7.0 4.0 9.0 9.0
## 2028 7.0 9.0 8.0 9.0 8.0
## 2029 7.0 7.0 6.0 8.0 9.0
## 2030 5.0 5.0 5.0 9.0 7.0
## 2031 2.0 6.0 1.0 9.0 8.0
## 2032 4.0 7.0 6.0 9.0 9.0
## 2033 6.0 9.0 6.0 10.0 8.0
## 2034 1.0 3.0 1.0 10.0 8.0
## 2035 6.0 8.0 6.0 9.0 9.0
## 2036 6.0 7.0 6.0 7.0 9.0
## 2037 5.0 8.0 7.0 9.0 9.0
## 2038 2.0 7.0 2.0 10.0 9.0
## 2039 3.0 10.0 3.0 9.0 8.0
## 2040 2.0 3.0 1.0 9.0 8.0
## 2041 8.0 7.0 5.0 7.0 7.0
## 2042 5.0 7.0 5.0 5.0 7.0
## 2043 5.0 7.0 4.0 8.0 8.0
## 2044 10.0 10.0 8.0 8.0 8.0
## 2045 8.0 7.0 6.0 6.0 8.0
## 2046 5.0 5.0 4.0 6.0 6.0
## 2047 2.0 7.0 1.0 7.0 6.0
## 2048 4.0 6.0 7.0 8.0 9.0
## 2049 8.0 8.0 6.0 8.0 9.0
## 2050 4.0 6.0 5.0 8.0 8.0
## 2051 4.0 7.0 6.0 7.0 7.0
## 2052 8.0 7.0 5.0 7.0 7.0
## 2053 8.0 9.0 5.0 7.0 8.0
## 2054 3.0 6.0 2.0 7.0 9.0
## 2055 6.0 10.0 3.0 6.0 7.0
## 2056 6.0 5.0 4.0 6.0 10.0
## 2057 8.0 10.0 6.0 6.0 9.0
## 2058 8.0 7.0 4.0 4.0 9.0
## 2059 8.0 8.0 7.0 6.0 8.0
## 2060 10.0 7.0 5.0 4.0 8.0
## 2061 8.0 8.0 6.0 5.0 8.0
## 2062 8.0 9.0 7.0 8.0 10.0
## 2063 8.0 8.0 6.0 7.0 8.0
## 2064 8.0 8.0 6.0 6.0 9.0
## 2065 7.0 7.0 2.0 7.0 10.0
## 2066 7.0 7.0 6.0 8.0 8.0
## 2067 7.0 7.0 4.0 7.0 9.0
## 2068 6.0 6.0 6.0 7.0 9.0
## 2069 9.0 9.0 9.0 8.0 8.0
## 2070 7.0 6.0 6.0 7.0 8.0
## 2071 9.0 10.0 10.0 8.0 10.0
## 2072 10.0 9.0 8.0 10.0 8.0
## 2073 8.0 8.0 7.0 8.0 7.0
## 2074 9.0 8.0 6.0 5.0 10.0
## 2075 10.0 7.0 9.0 6.0 8.0
## 2076 9.0 8.0 7.0 6.0 8.0
## 2077 7.0 7.0 6.0 5.0 6.0
## 2078 6.0 6.0 5.0 7.0 8.0
## 2079 5.0 5.0 1.0 6.0 6.0
## 2080 8.0 6.0 8.0 7.0 6.0
## 2081 8.0 8.0 7.0 8.0 8.0
## 2082 8.0 7.0 7.0 10.0 8.0
## 2083 8.0 9.0 9.0 7.0 10.0
## 2084 7.0 7.0 6.0 7.0 9.0
## 2085 9.0 8.0 6.0 9.0 9.0
## 2086 8.0 7.0 5.0 7.0 8.0
## 2087 7.0 9.0 5.0 7.0 9.0
## 2088 7.0 6.0 7.0 8.0 8.0
## 2089 8.0 8.0 5.0 6.0 7.0
## 2090 8.0 9.0 6.0 3.0 6.0
## 2091 8.0 7.0 7.0 8.0 7.0
## 2092 8.0 9.0 8.0 5.0 6.0
## 2093 7.0 8.0 6.0 4.0 6.0
## 2094 6.0 5.0 4.0 5.0 7.0
## 2095 4.0 9.0 7.0 6.0 6.0
## 2096 4.0 8.0 6.0 8.0 6.0
## 2097 6.0 6.0 5.0 6.0 9.0
## 2098 7.0 6.0 9.0 8.0 7.0
## 2099 7.0 7.0 8.0 6.0 7.0
## 2100 6.0 7.0 6.0 5.0 7.0
## 2101 7.0 7.0 2.0 7.0 6.0
## 2102 4.0 6.0 3.0 6.0 5.0
## 2103 3.0 8.0 10.0 5.0 7.0
## 2104 2.0 5.0 2.0 5.0 5.0
## 2105 8.0 8.0 7.0 7.0 7.0
## 2106 8.0 9.0 8.0 5.0 8.0
## 2107 8.0 7.0 7.0 8.0 8.0
## 2108 7.0 9.0 7.0 6.0 7.0
## 2109 8.0 7.0 6.0 5.0 8.0
## 2110 7.0 7.0 8.0 7.0 9.0
## 2111 8.0 8.0 7.0 8.0 8.0
## 2112 4.0 4.0 4.0 8.0 8.0
## 2113 8.0 6.0 6.0 9.0 9.0
## 2114 8.0 7.0 7.0 8.0 9.0
## 2115 7.0 6.0 8.0 6.0 8.0
## 2116 8.0 6.0 7.0 8.0 10.0
## 2117 9.0 8.0 6.0 8.0 10.0
## 2118 6.0 5.0 7.0 8.0 6.0
## 2119 7.0 10.0 8.0 8.0 9.0
## 2120 5.0 5.0 4.0 7.0 6.0
## 2121 8.0 5.0 6.0 8.0 7.0
## 2122 8.0 8.0 6.0 4.0 9.0
## 2123 7.0 6.0 7.0 7.0 7.0
## 2124 9.0 8.0 9.0 4.0 8.0
## 2125 10.0 8.0 8.0 9.0 10.0
## 2126 6.0 6.0 5.0 9.0 6.0
## 2127 6.0 7.0 2.0 7.0 7.0
## 2128 3.0 3.0 3.0 6.0 6.0
## 2129 7.0 6.0 7.0 9.0 10.0
## 2130 5.0 7.0 8.0 9.0 9.0
## 2131 7.0 7.0 9.0 8.0 9.0
## 2132 7.0 7.0 6.0 7.0 8.0
## 2133 9.0 7.0 5.0 8.0 7.0
## 2134 7.0 8.0 8.0 5.0 10.0
## 2135 6.0 10.0 6.0 8.0 8.0
## 2136 7.0 7.0 6.0 8.0 7.0
## 2137 6.0 5.0 7.0 8.0 8.0
## 2138 10.0 6.0 6.0 6.0 9.0
## 2139 5.0 6.0 5.0 7.0 8.0
## 2140 7.0 5.0 4.0 6.0 8.0
## 2141 7.0 7.0 7.0 8.0 7.0
## 2142 5.0 6.0 5.0 7.0 8.0
## 2143 1.0 5.0 2.0 8.0 8.0
## 2144 4.0 4.0 6.0 9.0 7.0
## 2145 7.0 8.0 6.0 8.0 10.0
## 2146 4.0 6.0 5.0 10.0 9.0
## 2147 8.0 8.0 9.0 8.0 9.0
## 2148 5.0 7.0 6.0 7.0 8.0
## 2149 5.0 5.0 4.0 10.0 8.0
## 2150 6.0 4.0 5.0 9.0 9.0
## 2151 7.0 8.0 6.0 7.0 8.0
## 2152 4.0 5.0 4.0 9.0 7.0
## 2153 8.0 9.0 5.0 4.0 4.0
## 2154 8.0 8.0 6.0 3.0 7.0
## 2155 7.0 7.0 6.0 7.0 7.0
## 2156 7.0 7.0 4.0 5.0 7.0
## 2157 6.0 7.0 6.0 7.0 7.0
## 2158 6.0 8.0 7.0 8.0 8.0
## 2159 5.0 5.0 2.0 8.0 8.0
## 2160 3.0 7.0 8.0 7.0 7.0
## 2161 5.0 8.0 6.0 5.0 7.0
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## 4962 7.0 6.0 5.0
## 4963 7.0 5.0 6.0
## 4965 10.0 7.0 10.0
## 4966 7.0 6.0 7.0
## 4967 10.0 8.0 7.0
## 4968 9.0 9.0 8.0
## 4969 10.0 9.0 10.0
## 4970 10.0 8.0 8.0
## 4971 8.0 8.0 8.0
## 4972 8.0 8.0 8.0
## 4973 6.0 10.0 7.0
## 4974 10.0 9.0 10.0
## 4975 9.0 8.0 8.0
## 4976 10.0 8.0 8.0
## 4977 9.0 9.0 9.0
## 4978 10.0 8.0 8.0
## 4979 8.0 8.0 9.0
## 4980 10.0 5.0 9.0
## 4981 8.0 8.0 8.0
## 4982 9.0 8.0 8.0
## 4983 5.0 2.0 8.0
## 4984 9.0 7.0 8.0
## 4985 6.0 3.0 7.0
## 4986 8.0 7.0 8.0
## 4987 8.0 7.0 8.0
## 4988 8.0 8.0 7.0
## 4989 7.0 8.0 5.0
## 4990 6.0 8.0 7.0
## 4992 7.0 6.0 6.0
## 4993 7.0 8.0 7.0
## 4994 8.0 7.0 6.0
## 4995 8.0 7.0 9.0
## 4996 8.0 5.0 7.0
## 4997 5.0 10.0 7.0
## 4998 9.0 2.0 10.0
## 4999 7.0 4.0 5.0
## 5000 6.0 6.0 5.0
## 5001 9.0 4.0 9.0
## 5002 8.0 6.0 6.0
## 5003 8.0 5.0 5.0
## 5004 6.0 6.0 5.0
## 5005 7.0 9.0 7.0
## 5006 9.0 7.0 7.0
## 5007 9.0 6.0 8.0
## 5008 9.0 8.0 6.0
## 5009 4.0 4.0 5.0
## 5010 8.0 8.0 8.0
## 5011 7.0 7.0 5.0
## 5012 7.0 6.0 6.0
## 5013 7.0 7.0 9.0
## 5014 7.0 4.0 8.0
## 5015 6.0 6.0 5.0
## 5016 8.0 7.0 9.0
## 5017 6.0 4.0 6.0
## 5018 7.0 8.0 7.0
## 5020 7.0 7.0 7.0
## 5021 7.0 7.0 7.0
## 5022 8.0 8.0 8.0
## 5023 6.0 6.0 7.0
## 5024 8.0 8.0 8.0
## 5025 7.0 7.0 7.0
## 5026 7.0 7.0 7.0
## 5027 8.0 7.0 7.0
## 5028 8.0 7.0 7.0
## 5029 3.0 2.0 3.0
## 5030 6.0 7.0 7.0
## 5031 9.0 9.0 9.0
## 5032 7.0 7.0 7.0
## 5033 8.0 8.0 8.0
## 5034 8.0 7.0 7.0
## 5035 7.0 5.0 7.0
## 5036 7.0 7.0 7.0
## 5037 7.0 6.0 6.0
## 5038 8.0 7.0 5.0
## 5039 8.0 7.0 7.0
## 5040 8.0 8.0 6.0
## 5041 7.0 7.0 7.0
## 5042 8.0 9.0 6.0
## 5043 7.0 7.0 6.0
## 5044 8.0 9.0 7.0
## 5045 7.0 9.0 7.0
## 5046 8.0 8.0 7.0
## 5047 8.0 7.0 8.0
## 5048 9.0 7.0 7.0
## 5049 7.0 7.0 7.0
## 5050 7.0 6.0 6.0
## 5051 8.0 8.0 8.0
## 5052 8.0 8.0 8.0
## 5053 6.0 5.0 5.0
## 5054 6.0 5.0 5.0
## 5055 5.0 4.0 5.0
## 5056 8.0 5.0 6.0
## 5057 8.0 7.0 6.0
## 5058 8.0 8.0 6.0
## 5059 7.0 6.0 6.0
## 5060 7.0 5.0 6.0
## 5063 8.0 7.0 7.0
## 5064 7.0 7.0 7.0
## 5065 7.0 7.0 7.0
## 5066 8.0 8.0 8.0
## 5067 8.0 7.0 7.0
## 5068 7.0 6.0 6.0
## 5069 6.0 5.0 5.0
## 5070 7.0 6.0 5.0
## 5071 7.0 4.0 6.0
## 5072 6.0 4.0 6.0
## 5073 4.0 6.0 6.0
## 5074 10.0 10.0 7.0
## 5075 4.0 3.0 5.0
## 5076 8.0 8.0 7.0
## 5077 8.0 9.0 7.0
## 5078 10.0 10.0 9.0
## 5079 6.0 4.0 4.0
## 5080 9.0 9.0 10.0
## 5081 8.0 10.0 6.0
## 5082 9.0 9.0 9.0
## 5083 10.0 10.0 10.0
## 5084 7.0 7.0 6.0
## 5085 10.0 10.0 9.0
## 5087 6.0 6.0 6.0
## 5088 10.0 3.0 10.0
## 5089 10.0 4.0 4.0
## 5090 8.0 10.0 7.0
## 5091 7.0 9.0 8.0
## 5092 9.0 8.0 8.0
## 5093 7.0 7.0 8.0
## 5094 9.0 9.0 8.0
## 5095 9.0 9.0 9.0
## 5096 7.0 8.0 8.0
## 5097 8.0 6.0 7.0
## 5098 5.0 5.0 5.0
## 5101 8.0 8.0 8.0
## 5102 7.0 8.0 5.0
## 5103 8.0 7.0 8.0
## 5105 7.0 7.0 7.0
## 5106 9.0 9.0 8.0
## 5107 8.0 9.0 8.0
## 5108 6.0 5.0 6.0
## 5109 7.0 5.0 7.0
## 5112 7.0 6.0 6.0
## 5113 7.0 7.0 6.0
## 5115 7.0 6.0 7.0
## 5116 7.0 7.0 7.0
## 5119 7.0 5.0 6.0
## 5122 7.0 4.0 7.0
## 5123 6.0 5.0 7.0
## 5124 7.0 7.0 8.0
## 5127 6.0 0.0 3.0
## 5128 7.0 4.0 5.0
## 5129 7.0 4.0 4.0
## 5130 6.0 7.0 6.0
## 5131 8.0 8.0 8.0
## 5132 10.0 7.0 7.0
## 5133 6.0 6.0 6.0
## 5134 8.0 9.0 7.0
## 5136 8.0 8.0 8.0
## 5137 7.0 6.0 7.0
## 5138 7.0 5.0 5.0
## 5139 7.0 6.0 9.0
## 5140 8.0 6.0 8.0
## 5141 7.0 5.0 5.0
## 5142 9.0 7.0 9.0
## 5143 7.0 0.0 4.0
## 5144 6.0 2.0 2.0
## 5145 8.0 6.0 8.0
## 5146 7.0 7.0 7.0
## 5147 7.0 7.0 7.0
## 5148 7.0 7.0 7.0
## 5150 8.0 8.0 8.0
## 5151 8.0 7.0 7.0
## 5152 7.0 7.0 7.0
## 5153 7.0 8.0 7.0
## 5154 8.0 7.0 7.0
## 5155 7.0 7.0 7.0
## 5156 7.0 7.0 7.0
## 5157 8.0 7.0 7.0
## 5158 7.0 7.0 7.0
## 5159 8.0 8.0 8.0
## 5160 10.0 10.0 10.0
## 5161 8.0 7.0 7.0
## 5162 7.0 6.0 7.0
## 5163 8.0 6.0 7.0
## 5164 8.0 7.0 7.0
## 5165 5.0 6.0 6.0
## 5166 7.0 4.0 4.0
## 5168 9.0 9.0 7.0
## 5169 7.0 8.0 7.0
## 5171 7.0 6.0 6.0
## 5172 7.0 7.0 5.0
## 5173 7.0 7.0 7.0
## 5174 6.0 6.0 7.0
## 5175 7.0 7.0 7.0
## 5176 5.0 5.0 6.0
## 5177 6.0 6.0 7.0
## 5178 7.0 7.0 5.0
## 5179 7.0 4.0 7.0
## 5180 7.0 7.0 2.0
## 5181 5.0 2.0 6.0
## 5182 6.0 5.0 5.0
## 5183 5.0 5.0 6.0
## 5184 7.0 7.0 8.0
## 5185 7.0 6.0 7.0
## 5186 6.0 7.0 7.0
## 5187 6.0 7.0 7.0
## 5188 7.0 7.0 8.0
## 5189 7.0 7.0 6.0
## 5191 6.0 6.0 6.0
## 5192 6.0 4.0 4.0
## 5194 7.0 5.0 6.0
## 5195 6.0 6.0 5.0
## 5196 7.0 5.0 7.0
## 5197 5.0 5.0 5.0
## 5198 7.0 6.0 6.0
## 5199 9.0 7.0 7.0
## 5201 10.0 8.0 9.0
## 5202 10.0 9.0 10.0
## 5204 7.0 4.0 3.0
## 5205 8.0 8.0 9.0
## 5206 10.0 10.0 10.0
## 5207 10.0 9.0 8.0
## 5208 9.0 5.0 5.0
## 5209 6.0 6.0 6.0
## 5210 4.0 3.0 3.0
## 5211 9.0 10.0 9.0
## 5212 6.0 7.0 7.0
## 5213 9.0 8.0 9.0
## 5214 9.0 5.0 5.0
## 5215 9.0 5.0 10.0
## 5216 9.5 7.0 7.0
## 5217 9.0 8.0 9.0
## 5218 8.0 8.0 8.0
## 5219 7.0 7.0 7.0
## 5220 7.0 7.0 7.0
## 5221 8.0 8.0 8.0
## 5222 8.0 7.0 7.0
## 5224 7.0 6.0 6.0
## 5225 8.0 9.0 8.0
## 5226 8.0 7.0 7.0
## 5227 8.0 7.0 7.0
## 5228 7.0 6.0 6.0
## 5229 8.0 6.0 6.0
## 5230 8.0 8.0 8.0
## 5231 8.0 8.0 8.0
## 5233 7.0 7.0 7.0
## 5234 8.0 7.0 8.0
## 5235 8.0 7.0 7.0
## 5236 8.0 7.0 7.0
## 5237 7.0 6.0 5.0
## 5238 8.0 7.0 8.0
## 5239 7.0 6.0 8.0
## 5240 7.0 6.0 8.0
## 5241 7.0 7.0 6.0
## 5242 4.0 3.0 2.0
## 5243 9.0 7.0 7.0
## 5244 7.0 8.0 8.0
## 5245 8.0 8.0 7.0
## 5246 6.0 6.0 7.0
## 5247 7.0 7.0 7.0
## 5248 6.0 5.0 6.0
## 5249 8.0 8.0 8.0
## 5250 8.0 7.0 8.0
## 5252 7.0 7.0 7.0
## 5253 7.0 7.0 8.0
## 5254 8.0 6.0 8.0
## 5255 6.0 5.0 5.0
## 5256 8.0 9.0 8.0
## 5257 7.0 9.0 8.0
## 5258 8.0 6.0 8.0
## 5259 8.0 10.0 8.0
## 5260 9.0 9.0 9.0
## 5261 6.0 5.0 8.0
## 5262 8.0 10.0 8.0
## 5263 9.0 8.0 9.0
## 5264 9.0 8.0 9.0
## 5265 9.0 9.0 9.0
## 5266 9.0 8.0 8.0
## 5267 7.0 6.0 6.0
## 5268 7.0 9.0 8.0
## 5269 9.0 8.0 9.0
## 5270 9.0 4.0 9.0
## 5271 9.0 9.0 9.0
## 5272 9.0 5.0 10.0
## 5273 10.0 8.0 10.0
## 5274 8.0 6.0 7.0
## 5275 7.0 8.0 6.0
## 5276 6.0 7.0 6.0
## 5277 9.0 8.0 9.0
## 5278 7.0 7.0 7.0
## 5279 6.0 6.0 5.0
## 5280 8.0 4.0 3.0
## 5281 9.0 5.0 7.0
## 5282 6.0 5.0 6.0
## 5283 6.0 7.0 6.0
## 5284 6.0 5.0 5.0
## 5285 7.0 4.0 6.0
## 5286 7.0 5.0 7.0
## 5287 7.0 8.0 7.0
## 5288 6.0 8.0 5.0
## 5289 8.0 8.0 8.0
## 5290 8.0 9.0 7.0
## 5293 7.0 5.0 6.0
## 5294 8.0 8.0 7.0
## 5295 6.0 6.0 6.0
## 5296 6.0 7.0 5.0
## 5297 9.0 8.0 9.0
## 5298 6.0 5.0 5.0
## 5299 7.0 5.0 6.0
## 5300 6.0 6.0 5.0
## 5301 8.0 9.0 8.0
## 5302 7.0 8.0 7.0
## 5303 8.0 6.0 6.0
## 5304 7.0 6.0 5.0
## 5305 6.0 4.0 3.0
## 5306 7.0 9.0 8.0
## 5307 9.0 9.0 9.0
## 5309 8.0 8.0 8.0
## 5310 8.0 8.0 8.0
## 5311 8.0 8.0 6.0
## 5312 8.0 6.0 9.0
## 5313 7.0 7.0 6.0
## 5314 6.0 9.0 8.0
## 5315 6.0 4.0 6.0
## 5316 6.0 5.0 6.0
## 5317 7.0 8.0 6.0
## 5319 7.0 6.0 6.0
## 5320 7.0 7.0 7.0
## 5321 6.0 7.0 6.0
## 5322 5.0 4.0 4.0
## 5323 4.0 3.0 5.0
## shared_interests_partner like guess_prob_liked met decision
## 1 5.0 7.0 6.0 0 1
## 2 6.0 7.0 5.0 1 1
## 3 7.0 7.0 5.0 1 1
## 4 8.0 7.0 6.0 0 1
## 5 6.0 6.0 6.0 0 1
## 6 4.0 6.0 5.0 0 0
## 7 7.0 6.0 5.0 0 1
## 8 6.0 6.0 7.0 0 0
## 9 8.0 7.0 7.0 0 1
## 10 8.0 6.0 6.0 0 1
## 11 3.0 6.0 4.0 0 0
## 12 6.0 7.0 3.0 0 0
## 13 4.0 6.0 7.0 0 0
## 14 7.0 7.0 8.0 0 1
## 15 8.0 8.0 6.0 1 0
## 16 2.0 6.0 5.0 0 0
## 17 9.0 8.0 7.0 0 1
## 18 5.0 5.0 6.0 0 0
## 19 5.0 5.0 6.0 0 1
## 20 8.0 8.0 7.0 0 1
## 21 9.0 8.0 7.0 1 0
## 22 7.0 8.0 7.0 0 0
## 23 7.0 8.0 7.0 0 0
## 24 7.0 8.0 7.0 0 0
## 25 10.0 9.0 5.0 0 0
## 26 9.0 8.0 5.0 0 0
## 27 9.0 8.0 7.0 0 0
## 28 7.0 8.0 7.0 0 0
## 29 9.0 9.0 7.0 0 0
## 30 7.0 8.0 7.0 0 0
## 31 7.0 6.0 7.0 0 0
## 32 8.0 8.0 1.0 1 0
## 33 7.0 4.0 1.0 0 0
## 34 10.0 8.0 10.0 1 1
## 35 9.0 7.0 7.0 0 0
## 36 2.0 4.0 3.0 0 0
## 37 5.0 5.0 1.0 0 0
## 38 7.0 6.0 6.0 0 0
## 39 8.0 8.0 8.0 0 1
## 40 8.0 10.0 8.0 0 1
## 41 2.0 7.0 5.0 0 0
## 42 5.0 6.0 4.0 1 0
## 43 5.0 6.0 5.0 1 0
## 44 9.0 8.0 2.0 1 1
## 45 8.0 7.0 4.0 0 0
## 46 8.0 7.0 3.0 0 1
## 47 6.0 6.0 3.0 0 1
## 48 5.0 6.0 4.0 0 1
## 49 9.0 9.0 4.0 0 1
## 50 5.0 10.0 3.0 0 1
## 51 6.0 6.0 6.0 0 1
## 52 2.0 7.0 6.0 0 1
## 53 2.0 6.0 2.0 1 0
## 54 8.0 9.0 4.0 0 1
## 55 2.0 4.0 3.0 1 0
## 56 1.0 2.0 2.0 0 0
## 57 4.0 8.0 7.0 0 1
## 58 3.0 5.0 5.0 0 0
## 59 9.0 9.0 6.0 0 1
## 60 7.0 9.0 8.0 0 1
## 61 4.0 7.0 4.0 0 0
## 62 4.0 7.0 3.0 1 0
## 63 8.0 7.0 8.0 1 0
## 64 6.0 8.0 6.0 1 1
## 65 5.0 5.0 6.0 1 0
## 66 4.0 5.0 5.0 0 0
## 67 7.0 8.0 7.0 0 1
## 68 5.0 5.0 7.0 0 0
## 69 6.0 6.0 6.0 0 1
## 70 6.0 7.0 5.0 0 1
## 71 5.0 4.0 5.0 0 1
## 72 7.0 8.0 7.0 0 1
## 73 7.0 5.0 5.0 0 1
## 74 5.0 5.0 6.0 0 1
## 75 7.0 6.0 5.0 0 1
## 76 7.0 6.0 5.0 0 1
## 77 7.0 6.0 6.0 0 1
## 78 6.0 5.0 4.0 0 1
## 79 9.0 7.0 10.0 0 1
## 80 8.0 8.0 7.0 0 1
## 81 6.0 5.0 5.0 0 1
## 82 10.0 10.0 5.0 0 1
## 83 6.0 8.0 5.0 0 1
## 84 7.0 7.0 5.0 0 1
## 85 5.0 5.0 5.0 0 1
## 86 5.0 6.0 5.0 0 1
## 87 6.0 7.0 5.0 0 1
## 88 6.0 7.0 5.0 0 1
## 89 5.0 7.0 6.0 0 1
## 90 8.0 8.0 5.0 0 1
## 91 6.0 6.0 3.0 0 0
## 92 6.0 1.0 1.0 1 0
## 93 6.0 7.0 6.0 0 1
## 94 6.0 10.0 1.0 1 0
## 95 6.0 6.0 1.0 0 0
## 96 6.0 6.0 1.0 0 0
## 97 6.0 7.0 1.0 0 1
## 98 6.0 5.0 5.0 0 0
## 99 9.0 9.0 9.0 0 1
## 100 8.0 9.0 10.0 0 1
## 101 6.0 7.0 4.0 0 0
## 102 4.0 7.0 2.0 0 0
## 103 4.0 5.0 2.0 1 0
## 104 5.0 6.0 3.0 0 0
## 105 6.0 6.0 5.0 0 0
## 106 4.0 7.0 4.0 0 0
## 107 7.0 7.0 4.0 0 0
## 108 5.0 7.0 4.0 0 0
## 109 5.0 6.0 4.0 0 0
## 110 5.0 6.0 3.0 0 0
## 111 5.0 8.0 4.0 0 0
## 112 5.0 8.0 4.0 0 0
## 113 5.0 7.0 3.0 0 0
## 114 5.0 6.0 3.0 1 0
## 115 5.0 7.0 4.0 1 0
## 116 6.0 7.0 4.0 0 0
## 117 5.0 8.0 4.0 1 1
## 118 5.0 9.0 4.0 0 1
## 119 5.0 8.0 4.0 0 1
## 120 1.0 1.0 1.0 0 0
## 121 10.0 10.0 10.0 1 1
## 122 10.0 10.0 10.0 0 1
## 123 10.0 10.0 10.0 0 1
## 124 10.0 10.0 10.0 0 1
## 125 10.0 10.0 10.0 1 1
## 126 10.0 10.0 10.0 1 1
## 127 10.0 10.0 10.0 0 1
## 128 10.0 10.0 10.0 0 1
## 129 10.0 10.0 10.0 0 1
## 130 10.0 10.0 10.0 0 1
## 131 8.0 7.0 7.0 0 1
## 132 9.0 9.0 7.0 0 1
## 133 8.0 7.0 7.0 0 1
## 134 9.0 8.0 10.0 1 1
## 135 10.0 7.0 7.0 1 1
## 136 8.0 7.0 7.0 0 1
## 137 9.0 8.0 7.0 1 1
## 138 7.0 8.0 7.0 0 1
## 139 7.0 7.0 7.0 0 1
## 140 8.0 7.0 7.0 1 1
## 141 7.0 8.0 6.0 0 1
## 142 8.0 10.0 1.0 1 1
## 143 6.0 6.0 1.0 0 1
## 144 9.0 9.0 7.0 0 1
## 145 5.0 5.0 7.0 0 0
## 146 6.0 9.0 2.0 1 1
## 147 5.0 8.0 4.0 0 1
## 148 7.0 9.0 7.0 0 1
## 149 7.0 7.0 8.0 0 1
## 150 6.0 6.0 6.0 0 1
## 151 7.0 7.0 6.0 0 1
## 152 7.0 7.0 5.0 0 1
## 153 6.0 6.0 7.0 0 1
## 154 7.0 6.0 6.0 0 1
## 155 7.0 6.0 7.0 0 0
## 156 7.0 6.0 6.0 0 1
## 157 6.0 7.0 7.0 0 1
## 158 7.0 7.0 6.0 0 1
## 159 7.0 6.0 5.0 0 1
## 160 7.0 6.0 6.0 0 0
## 161 7.0 2.0 1.0 0 0
## 162 3.0 4.0 3.0 0 0
## 163 4.0 2.0 2.0 0 0
## 164 5.0 6.0 4.0 0 1
## 165 3.0 3.0 3.0 0 0
## 166 4.0 3.0 3.0 0 0
## 167 3.0 5.0 4.0 0 0
## 168 4.0 4.0 4.0 0 0
## 169 5.0 8.0 4.0 0 1
## 170 4.0 3.0 6.0 0 0
## 171 6.0 7.0 5.0 0 0
## 172 5.0 7.0 6.0 0 1
## 173 4.0 5.0 1.0 0 0
## 174 5.0 7.0 7.0 0 0
## 175 5.0 6.0 5.0 0 0
## 176 5.0 7.0 6.0 0 0
## 177 5.0 7.0 5.0 0 1
## 178 5.0 7.0 5.0 0 1
## 179 5.0 7.0 5.0 0 0
## 180 5.0 7.0 6.0 0 0
## 181 9.0 6.5 8.0 0 1
## 182 7.0 8.0 8.0 0 1
## 183 7.0 7.0 7.0 0 1
## 184 10.0 8.0 8.0 0 1
## 185 6.0 7.0 7.0 0 1
## 186 6.0 6.0 6.0 0 1
## 187 10.0 9.0 9.0 0 1
## 188 10.0 8.0 8.0 0 1
## 189 6.0 6.0 6.0 0 1
## 190 6.0 7.0 8.0 0 1
## 191 6.0 6.0 6.0 0 0
## 192 7.0 6.0 9.0 0 0
## 193 6.0 5.0 5.0 0 0
## 194 7.0 7.0 7.0 0 0
## 195 5.0 4.0 10.0 0 0
## 196 7.0 6.0 7.0 0 0
## 197 7.0 7.0 7.0 0 0
## 198 8.0 8.0 8.0 0 1
## 199 6.0 6.0 6.0 0 0
## 200 6.0 7.0 7.0 0 0
## 201 7.0 9.0 1.0 1 0
## 202 5.0 7.0 2.0 0 0
## 203 6.0 9.0 3.0 0 1
## 204 3.0 6.0 1.0 0 0
## 205 2.0 6.0 1.0 0 0
## 206 4.0 5.0 2.0 0 0
## 207 5.0 8.0 2.0 0 0
## 208 3.0 5.0 1.0 0 0
## 209 3.0 6.0 1.0 0 0
## 210 8.0 9.0 5.0 0 1
## 211 6.0 6.0 1.0 0 0
## 212 2.0 2.0 1.0 0 0
## 213 2.0 6.0 1.0 0 0
## 214 2.0 6.0 1.0 0 0
## 215 2.0 6.0 1.0 0 0
## 216 5.0 8.0 3.0 0 0
## 217 5.0 6.0 4.0 0 0
## 218 2.0 3.0 3.0 0 0
## 219 3.0 3.0 2.0 0 0
## 220 3.0 4.0 3.0 0 0
## 221 6.0 6.0 6.0 0 1
## 222 6.0 6.0 6.0 0 1
## 223 7.0 7.0 4.0 0 1
## 224 5.0 5.0 4.0 0 0
## 225 6.0 7.0 4.0 0 1
## 226 4.0 5.0 3.0 0 0
## 227 7.0 6.0 6.0 0 1
## 228 2.0 2.0 1.0 0 0
## 229 4.0 2.0 2.0 0 0
## 230 8.0 8.0 5.0 0 1
## 231 4.0 4.0 2.0 0 0
## 232 7.0 7.0 6.0 0 1
## 233 7.0 8.0 6.0 0 1
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## 1913 1.0 2.0 3.0 0 0
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## 1917 9.0 8.0 8.0 0 1
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## 1930 6.0 5.0 6.0 0 0
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## 1953 6.0 4.0 4.0 0 0
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## 1956 6.0 7.0 6.0 0 1
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## 1959 6.0 6.0 5.0 0 1
## 1960 6.0 5.0 5.0 0 0
## 1961 7.0 5.0 5.0 0 0
## 1962 5.0 7.0 5.0 0 0
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## 1971 5.0 5.0 5.0 0 0
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## 1983 8.0 8.0 8.0 0 0
## 1984 5.0 5.0 5.0 0 0
## 1985 5.0 5.0 5.0 0 0
## 1986 3.0 4.0 4.0 0 0
## 1987 3.0 4.0 4.0 0 0
## 1988 6.0 8.0 5.0 0 1
## 1989 6.0 8.0 5.0 0 0
## 1990 5.0 5.0 7.0 0 0
## 1991 7.0 6.0 6.0 0 0
## 1992 6.0 5.0 7.0 0 0
## 1993 3.0 3.0 3.0 0 0
## 1994 3.0 3.0 3.0 0 0
## 1995 10.0 8.0 10.0 1 1
## 1996 5.0 8.0 7.0 0 1
## 1997 10.0 4.0 1.0 0 0
## 1998 8.0 7.0 6.0 0 1
## 1999 6.0 6.0 6.0 0 0
## 2000 6.0 5.0 5.0 0 0
## 2001 6.0 7.0 6.0 0 0
## 2002 4.0 6.0 5.0 0 0
## 2003 5.0 7.0 7.0 0 0
## 2004 10.0 9.0 9.0 0 1
## 2005 8.0 3.0 2.0 0 0
## 2006 2.0 2.0 2.0 0 0
## 2007 7.0 8.0 8.0 0 1
## 2008 7.0 8.0 7.0 0 1
## 2009 1.0 1.0 3.0 0 0
## 2010 4.0 3.0 5.0 0 0
## 2011 8.0 7.0 8.0 1 0
## 2012 7.0 6.0 6.0 0 1
## 2013 2.0 1.0 4.0 0 0
## 2014 4.0 3.0 5.0 0 0
## 2015 6.0 6.0 6.0 0 0
## 2016 4.0 4.0 5.0 0 0
## 2017 6.0 7.0 7.0 0 0
## 2018 4.0 3.0 5.0 0 0
## 2019 7.0 2.0 6.0 0 0
## 2020 8.0 7.0 6.0 0 1
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## [ reached 'max' / getOption("max.print") -- omitted 2657 rows ]Stršeće vrijednosti
boxplots <- lapply(columns_of_interest, function(column_name) {
boxplot(filtered_dates[[column_name]], main = column_name)
})
filtered_dates$met[filtered_dates$met > 1] <- 1
unique(filtered_dates$met)## [1] 0 1Na temelju predefiniranih hipoteza možemo zaključiti da svi stupci u podatcima nisu jednako relevantni. U slučaju analize je li inteligencija partnera važnija od fizičkog izgleda od najveće su važnosti podaci vezani uz inteligenciju i fizički izgled. Analogno za analizu povezanosti gaminga i zanimanja participanta najvažniji su podaci vezani uz gaming i samo područje zanimanja participanta.
#TODO:tu ide prikaz podataka koji su relevantni, vizualizacija istih…histogram, scatter plot, box plot Promotrimo relevantne varijable koje ćemo kasnije koristiti u istraživačkim pitanjima. U vezi prvog pitanja bitni su nam inteligencija i fizički izgled partnera.
Je li inteligencija partnera ispitanicima važnija od izgleda?
Za ovo pitanje su nam bitni podaci o važnosti izgleda, odnosno inteligencije kod ispitanika. Prvo ćemo izvaditi sve null vrijednosti iz podataka.
cleaned_intelligence <- na.omit(participants$intelligence_important)
cleaned_attractiveness <- na.omit(participants$attractive_important)Te ćemo te podatke prikazati uz pomoć histograma.
hist(na.omit(participants$intelligence_important), main='Značajnost inteligenciju kod partnera', xlab='Intelligence', ylab='Frequency')
hist(na.omit(participants$attractive_important), main='Značajnost privlačnosti kod partnera', xlab='Attractiveness', ylab='Frequency')
Možemo vidjeti da je kod večine ljudi inteligencija bitnija od izgleda
kod partnera.
Podatke koje ćemo također analizirati su ocjene partnerove inteligencije i izgleda nakon speed datinga.
hist(dates$intelligence_o,main='Ocjena inteligencije', xlab='Inteligence grade', ylab='Frequency')
hist(dates$attractive_o,main='Ocjena izgleda', xlab='Attractivnes grade', ylab='Frequency')
Podatke o važnosti atraktivnosti i inteligencije ćemo usporediti u
histogramu.
merged_data <- inner_join(participants, dates, by = c("id" = "participant_id"))
b <- seq(0, 100, by = 5)
h1 <- hist(na.omit(participants$intelligence_important), breaks = b, plot = FALSE)
h2 <- hist(na.omit(participants$attractive_important), breaks = b, plot = FALSE)
data <- t(cbind(h1$counts, h2$counts))
barplot(data, beside = TRUE, col = c("purple", "lightgreen"), xlab = "Importance", ylab = 'Frequency')
legend("topright",c("intelligence","attractiveness"),fill = c( "purple", "lightgreen"))
Normalnost podataka ćemo provjeriti uz pomoć qqplot-ova i testom o
jednakosti varijanci.
qqnorm(na.omit(participants$attractive_important), pch = 1, frame = FALSE, main = "Važnost izgleda")
qqline(na.omit(participants$attractive_important), col = "blue", lwd = "2")
# QQ plot for intelligence_important without NA values
qqnorm(na.omit(participants$intelligence_important), pch = 1, frame = FALSE, main = "Važnost inteligencije")
qqline(na.omit(participants$intelligence_important), col = "blue", lwd = "2")
Uz pretpostavku normalnosti podataka možemo nastaviti testiranje varijanca.
Provest ćemo test o jednakosti varijanci uz pomoć F-testa. \[F = \frac{S_{X_1}^2 / \sigma_1^2}{S_{X_2}^2 / \sigma_2^2}\] Pri ćemu za statistike s1 (intelligence_important) i s2 (attractive_important) vrijedi : \[S_{X_1}^2 = \frac{1}{n_1 - 1} \sum_{i = 1}^{n_1} (X_1^i - \bar{X}_1)^2, \quad S_{X_2}^2 = \frac{1}{n_2 - 1} \sum_{i = 1}^{n_2} (X_2^i - \bar{X}_2)^2.\] Uz \((n_1 - 1, n_2 - 1)\) stupnjeva slobode. Hipoteze testa o jednakosti varijanca glase : \[ \begin{aligned} H_0&: \sigma_1^2 = \sigma_2^2 \\ H_1&: \sigma_1^2 \neq \sigma_2^2 \end{aligned} \]
Varijanca važnosti izgleda:
var_attractive <- var(na.omit(participants$attractive_important))
cat("Varijanca od attractive_important:", var_attractive, "\n")## Varijanca od attractive_important: 162.2717Varijanca važnosti inteligencije:
var_intelligence <- var(na.omit(participants$intelligence_important))
cat("Varijanca od intelligence_important:", var_intelligence, "\n")## Varijanca od intelligence_important: 47.30045Te radimo F-test:
var.test(participants$attractive_important, participants$intelligence_important)##
## F test to compare two variances
##
## data: participants$attractive_important and participants$intelligence_important
## F = 3.4307, num df = 539, denom df = 539, p-value < 2.2e-16
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 2.897158 4.062401
## sample estimates:
## ratio of variances
## 3.430659Zaključujemo uz p-vrijednost od 2.2e-16 da su te varijance različite. Odbacujemo H0 hipotezu u koristi H1 te ćemo koristiti T-test za dva uzorka uz nejednake varijance.
T-test za dva auzorka uz nepoznate i nejednake varijance
\[ \begin{aligned} H_0&: Inteligencija \quad je \quad vaznija\quad od \quad izgleda \\ H_1&: Inteligencija \quad nije \quad važnija \quad od \quad izgleda \end{aligned} \]
t_test_result <- t.test(participants$intelligence_important,
participants$attractive_important,
var.equal = FALSE)
print(t_test_result)##
## Welch Two Sample t-test
##
## data: participants$intelligence_important and participants$attractive_important
## t = -3.7697, df = 828.62, p-value = 0.000175
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.571237 -1.125652
## sample estimates:
## mean of x mean of y
## 20.17174 22.52019Uz malu p-vrijednost od 0.000175 odbacujemo hipotezu H0 u korist H1.
#TODO:za gaming i zaposljenje mozemo napraviti box plot pomocu group by po zaposlenju (za svako zaposlenje novi box plot) #scatter plot za inteligenciju i fizicki izgled sa dvije razlicite boje
#TODO:trebamo dogovoriti kaj napraviti za trecu hipotezu
Inferencijalna analiza
Drugo istraživacko pitanje glasi : Postoji li razlika u izbirljivosti prema zanimanju sudionika?
Posljednje istraživačko pitanje : Možemo li temeljem drugih varijabli predvidjeti hoće li speed date biti uspješniji?
Kako bismo vidjeli može li se na temelju ocjena sudionika o njihovim partnerima predvidjeti je li spoj bio uspješan ili ne. Prvo idemo razraditi definiciju uspješnosti spoja. Spoj je usješan jedino ako je sudionik u spoju ocjenio cjelokupno iskustvo s 1, a nuespješan ako je ocjenjeno s 0. Definicija je nužno ovakva jer se može dogoditi situacija ako dvoje ljudi otudu na spoj, jednoj se osobi spoj svidio, a drugoj ne onda njihove ocjene mogu biti drugačije. Zbog toga smo odlučili na ovaj subjektivan pristup. Pošto je odluka za uspješnost spoja binarna, odlučili smo koristiti model logističke regresije.
Model logističke regresije je statistički model koji se koristi za analizu odnosa između jedne binarne zavisne promenljive varijable i više nezavisnih promenljivih varijabli. Sam model je oblika:
\[\begin{aligned} F(x'\beta) = \Lambda(x'\beta) = \frac{1}{1 + e^{-x'\beta}} \end{aligned}\]
Cilj je predviđanje vjerojatnosti da će zavisna promenljiva varijabla imati vrijednost 1 (uspjeh) ili 0 (neuspjeh) na osnovu linearnih kombinacija nezavisnih promenljivih varijabli ugurane u sigmoidalne funkciju. Model logističke regresije ima funkciju vjerodstojnosti:
\[\begin{aligned} L(\beta) = \prod_{i=1}^{N}(\Lambda(x_i'\beta))^{y_i}(1-\Lambda(x_i'\beta))^{1-y_i} \end{aligned}\]
ili u log obliku:
\[\begin{aligned} l(\beta) = \sum_{i=1}^{N}y_i log(\Lambda(x_i'\beta))+ \sum_{i=1}^{N}(1-y_i)log(1-\Lambda(x_i'\beta)) \end{aligned}\]
Ovaj problem nema konkretno rješenje, ali može se riješiti ili itterativno metodom gradijentnog spusta ili numerički Newton-Raphsonovom metodom. Logistička regresija omogućava interpretaciju utjecaja svake nezavisne vaijable primjenjive na log-odds vjerovatnosti, pružajući također mogućnost procjene vjerojatnosti klasifikacije.
Kako bismo vidjeli snagu ovog modela odlučili smo primjeniti unakrsnu provjeru, koja uvodne podatke na trening_set:testing_set u omjeru 70:30:
set.seed(123)
train_indices <- sample(1:nrow(filtered_dates), 0.7 * nrow(filtered_dates))
training_set <- filtered_dates[train_indices, ]
testing_set <- filtered_dates[-train_indices, ]Kod za učenje i rezultati učenja us vidljivi ovdje:
logistic_model <- glm(decision ~ attractive_o + sincere_o + intelligence_o + funny_o + ambitious_o + shared_interests_o + attractive_partner + sincere_partner + intelligence_partner + funny_partner + ambition_partner + shared_interests_partner + like + guess_prob_liked + met, data = training_set, family = "binomial")
summary(logistic_model)##
## Call:
## glm(formula = decision ~ attractive_o + sincere_o + intelligence_o +
## funny_o + ambitious_o + shared_interests_o + attractive_partner +
## sincere_partner + intelligence_partner + funny_partner +
## ambition_partner + shared_interests_partner + like + guess_prob_liked +
## met, family = "binomial", data = training_set)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.637039 0.296091 -22.416 < 2e-16 ***
## attractive_o -0.175244 0.022793 -7.689 1.49e-14 ***
## sincere_o 0.106820 0.027487 3.886 0.000102 ***
## intelligence_o 0.022466 0.034204 0.657 0.511286
## funny_o -0.023296 0.026316 -0.885 0.376037
## ambitious_o 0.063049 0.026290 2.398 0.016476 *
## shared_interests_o -0.015097 0.021668 -0.697 0.485956
## attractive_partner 0.446886 0.026612 16.792 < 2e-16 ***
## sincere_partner -0.192148 0.030204 -6.362 2.00e-10 ***
## intelligence_partner -0.007718 0.036748 -0.210 0.833647
## funny_partner 0.152550 0.029026 5.256 1.48e-07 ***
## ambition_partner -0.165961 0.028957 -5.731 9.97e-09 ***
## shared_interests_partner 0.098502 0.024201 4.070 4.70e-05 ***
## like 0.536944 0.035856 14.975 < 2e-16 ***
## guess_prob_liked 0.195548 0.020436 9.569 < 2e-16 ***
## met -0.062649 0.176937 -0.354 0.723281
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 7569.8 on 5543 degrees of freedom
## Residual deviance: 5155.6 on 5528 degrees of freedom
## AIC: 5187.6
##
## Number of Fisher Scoring iterations: 5library(pROC)## Type 'citation("pROC")' for a citation.##
## Attaching package: 'pROC'## The following objects are masked from 'package:stats':
##
## cov, smooth, varlibrary(pheatmap)
predictions <- predict(logistic_model, testing_set, type = "response")
# Convert predicted probabilities to binary predictions (0 or 1)
predicted_class <- ifelse(predictions > 0.5, 1, 0)
# Assuming 'response_variable' is the actual response variable in your data
actual_class <- testing_set$decision
# Confusion matrix
conf_matrix <- table(actual_class, predicted_class)
roc_curve <- roc(actual_class, predicted_class)## Setting levels: control = 0, case = 1## Setting direction: controls < casesplot(roc_curve, main = "ROC Curve", col = "blue", lwd = 2)
# Area under the ROC curve (AUC)
auc_value <- auc(roc_curve)
cat("AUC:", auc_value, "\n")## AUC: 0.7580196# Stylish Confusion Matrix using pheatmap
pheatmap(
conf_matrix,
main = "Matrica zabune",
fontsize_row = 12, # Adjust font size for row labels
fontsize_col = 12, # Adjust font size for column labels
cellwidth = 100, # Adjust cell width
cellheight = 100, # Adjust cell height
cluster_cols = FALSE,
cluster_rows = FALSE,
display_numbers=TRUE,
color = colorRampPalette(c("blue", "steelblue", "white"))(10), # Adjust color palette
show_rownames = TRUE, # Show row names (actual labels)
show_colnames = TRUE, # Show column names (predicted labels)
angle_col = 0, # Rotate column names for better visibility
number_color = "black", # Color of the text inside cells
number_format = "%.0f", # Format for the numbers (adjust as needed)
fontsize_number = 12 # Font size of the numbers inside cells
)
# Calculate accuracy
accuracy <- sum(diag(conf_matrix)) / sum(conf_matrix)
# Calculate precision
precision <- conf_matrix[2, 2] / sum(conf_matrix[, 2])
# Calculate recall
recall <- conf_matrix[2, 2] / sum(conf_matrix[2, ])
# Calculate F1 score
f1_score <- 2 * (precision * recall) / (precision + recall)
# ROC curve and AUC-ROC
library(pROC)
roc_curve <- roc(actual_class, predictions)## Setting levels: control = 0, case = 1
## Setting direction: controls < casesauc_roc <- auc(roc_curve)
conf_matrix## predicted_class
## actual_class 0 1
## 0 1082 259
## 1 301 734cat("Accuracy:", accuracy, "\n")## Accuracy: 0.7643098cat("Precision:", precision, "\n")## Precision: 0.7391742cat("Recall:", recall, "\n")## Recall: 0.7091787cat("F1 Score:", f1_score, "\n")## F1 Score: 0.7238659cat("AUC-ROC:", auc_roc, "\n")## AUC-ROC: 0.8453177Gdje vrijednost pojedinog parametra možemo i vidjeti na slijedećem grafu:
plot_model(logistic_model, vline.color = "red", transform = NULL, title="Vrijenost parametra za pojedinu značajku")
models <- list()
for (col in columns_of_interest) {
formula <- as.formula(paste("decision ~", paste(columns_of_interest[!columns_of_interest %in% col], collapse = "+")))
model <- glm(formula, data = training_set, family = "binomial")
models[[col]] <- model
}
custom_format <- function(x) {
if (abs(x) >= 0.001) {
return(sprintf("%.6f", x))
} else {
return(format(x, scientific = TRUE))
}
}
p_values <- numeric(length(columns_of_interest))
i = 1
for(col in columns_of_interest) {
p_value <- lrtest(logistic_model, models[[col]])$`Pr(>Chisq)`[2]
#print(p_value)
p_values[i] <- p_value
i <- i+1
}
p_values <- data.frame(column = columns_of_interest, p_value = p_values)
p_values <- p_values %>% mutate(p_value = sapply(p_value, custom_format))
p_values## column p_value
## 1 attractive_o 8.00326e-15
## 2 sincere_o 9.682401e-05
## 3 intelligence_o 0.511329
## 4 funny_o 0.375943
## 5 ambitious_o 0.016446
## 6 shared_interests_o 0.485965
## 7 attractive_partner 2.869158e-69
## 8 sincere_partner 1.499543e-10
## 9 intelligence_partner 0.833636
## 10 funny_partner 1.24288e-07
## 11 ambition_partner 7.847928e-09
## 12 shared_interests_partner 4.477376e-05
## 13 like 2.439639e-55
## 14 guess_prob_liked 3.646484e-22
## 15 met 0.723611logistic_model_better <- glm(decision ~ attractive_o + sincere_o + attractive_partner + sincere_partner + funny_partner + ambition_partner + shared_interests_partner + like + guess_prob_liked, data = training_set, family = "binomial")
logistic_model_better##
## Call: glm(formula = decision ~ attractive_o + sincere_o + attractive_partner +
## sincere_partner + funny_partner + ambition_partner + shared_interests_partner +
## like + guess_prob_liked, family = "binomial", data = training_set)
##
## Coefficients:
## (Intercept) attractive_o sincere_o
## -6.38054 -0.17740 0.12812
## attractive_partner sincere_partner funny_partner
## 0.44715 -0.19385 0.14922
## ambition_partner shared_interests_partner like
## -0.17280 0.09739 0.53697
## guess_prob_liked
## 0.19422
##
## Degrees of Freedom: 5543 Total (i.e. Null); 5534 Residual
## Null Deviance: 7570
## Residual Deviance: 5165 AIC: 5185lrtest(logistic_model, logistic_model_better)## Likelihood ratio test
##
## Model 1: decision ~ attractive_o + sincere_o + intelligence_o + funny_o +
## ambitious_o + shared_interests_o + attractive_partner + sincere_partner +
## intelligence_partner + funny_partner + ambition_partner +
## shared_interests_partner + like + guess_prob_liked + met
## Model 2: decision ~ attractive_o + sincere_o + attractive_partner + sincere_partner +
## funny_partner + ambition_partner + shared_interests_partner +
## like + guess_prob_liked
## #Df LogLik Df Chisq Pr(>Chisq)
## 1 16 -2577.8
## 2 10 -2582.5 -6 9.4412 0.1502Zaključak
zakljucak na kraju svega WOHOOO